{
 "nbformat": 4,
 "nbformat_minor": 5,
 "metadata": {
  "colab": {
   "name": "yuvraj_openenv_hackathon_submission_colab_t4.ipynb",
   "provenance": [],
   "gpuType": "T4"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  },
  "language_info": {
   "name": "python"
  },
  "accelerator": "GPU"
 },
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "# OpenEnv Hackathon Submission\n\nThis Colab notebook is cleaned up from the original export and tuned to run on a Google Colab T4 GPU. Run the cells from top to bottom."
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## Before you run\n\n1. In Colab, choose **Runtime > Change runtime type > T4 GPU**.\n2. Run the install cell once.\n3. Then run the rest of the notebook in order."
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": "%%capture\n!pip install -q unsloth transformers trl datasets accelerate bitsandbytes peft sentence-transformers chromadb pylint \"pydantic>=2\" matplotlib huggingface_hub\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": "import os\nimport platform\nimport torch\n\nos.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n\nassert torch.cuda.is_available(), \"Please switch Colab to a GPU runtime before running this notebook.\"\ngpu_name = torch.cuda.get_device_name(0)\nprint(f\"Python: {platform.python_version()}\")\nprint(f\"Torch: {torch.__version__}\")\nprint(f\"GPU: {gpu_name}\")\nif \"T4\" not in gpu_name:\n    print(\"Note: this notebook is tuned for a Colab T4, but should also work on similar NVIDIA GPUs.\")\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": "# -*- coding: utf-8 -*-\n\"\"\"yuvraj's-openenv-hackathon-submission.ipynb\n\nAutomatically generated by Colab.\n\nOriginal file is located at\n    https://colab.research.google.com/drive/1e7-liCo1kurbV-gmWU6FyGm9XRIjgMDm\n\"\"\"\n\n# cell 7 author.py \u2013 Final production version: stateful, evidence-driven, belief tracking\n\nimport re\nimport ast\nfrom dataclasses import dataclass, field\nfrom typing import List, Dict, Any, Optional\n\n@dataclass\nclass PersonaAuthor:\n    \"\"\"\n    Simulates a human developer with:\n    - Continuous belief (confidence)\n    - Evidence-based reasoning\n    - Conversation memory\n    - Code inspection awareness\n    \"\"\"\n\n    personality: str = \"defensive\"   # defensive | junior | collaborative\n    max_persuasion_rounds: int = 5\n\n    # Evidence weights\n    weight_test_pass: float = 0.5\n    weight_lint_clean: float = 0.2\n    weight_doc_found: float = 0.15\n    weight_explanation_quality: float = 0.15\n\n    # Personality thresholds\n    thresholds: Dict[str, float] = field(default_factory=lambda: {\n        \"defensive\": 0.7,\n        \"junior\": 0.3,\n        \"collaborative\": 0.5,\n    })\n\n    # Internal state\n    _confidence: float = 0.0\n    _conversation: List[Dict[str, Any]] = field(default_factory=list)\n    _pushback_count: int = 0\n    _last_evidence_score: float = 0.0\n    _stagnation_counter: int = 0\n\n    # ------------------------------------------------------------------\n    # Lifecycle\n    # ------------------------------------------------------------------\n    def __post_init__(self):\n        self.reset()\n\n    def reset(self):\n        self._confidence = 0.0\n        self._conversation.clear()\n        self._pushback_count = 0\n        self._last_evidence_score = 0.0\n        self._stagnation_counter = 0\n\n    # ------------------------------------------------------------------\n    # Main interaction\n    # ------------------------------------------------------------------\n    # Added weight for code change magnitude\n    weight_code_change: float = 0.1   # small change is better\n\n    def respond(self,\n                agent_comment: str = \"\",\n                agent_question: str = \"\",\n                test_results: Optional[str] = None,\n                lint_results: Optional[str] = None,\n                doc_results: Optional[str] = None,\n                proposed_fix: Optional[str] = None,\n                original_code: Optional[str] = None) -> str:\n\n        # Store conversation\n        self._conversation.append({\n            \"comment\": agent_comment,\n            \"question\": agent_question,\n            \"test\": test_results,\n            \"lint\": lint_results,\n            \"docs\": doc_results\n        })\n\n        # Extract structured evidence\n        evidence = self._extract_evidence(test_results, lint_results, doc_results)\n\n        # Code inspection\n        code_change = 0.0\n        if proposed_fix and original_code:\n            code_change = self._inspect_code(proposed_fix, original_code)\n            evidence[\"code_change\"] = code_change\n\n        # Explanation score\n        text = (agent_comment + \" \" + agent_question).lower()\n        explanation_score = self._score_explanation(text)\n\n        # Compute evidence score \u2013 now includes code change penalty (1 - change)\n        evidence_score = (\n            self.weight_test_pass * evidence.get(\"test_pass_ratio\", 0.0) +\n            self.weight_lint_clean * (1 - min(1.0, evidence.get(\"lint_errors\", 0)/10)) +\n            self.weight_doc_found * (1.0 if evidence.get(\"doc_found\") else 0.0) +\n            self.weight_explanation_quality * explanation_score +\n            self.weight_code_change * (1.0 - code_change)   # surgical fix rewarded\n        )\n\n        evidence_score = max(0.0, min(1.0, evidence_score))\n\n        # Detect improvement\n        delta = evidence_score - self._last_evidence_score\n        self._last_evidence_score = evidence_score\n\n        if delta > 0.05:\n            self._stagnation_counter = 0\n        else:\n            self._stagnation_counter += 1\n\n        # Update belief (momentum)\n        lr = 0.3\n        self._confidence = (1 - lr) * self._confidence + lr * evidence_score\n\n        # Penalise stagnation\n        if self._stagnation_counter >= 2:\n            self._confidence *= 0.9\n\n        # Decision\n        threshold = self.thresholds.get(self.personality, 0.5)\n\n        if self._confidence >= threshold or self._pushback_count >= self.max_persuasion_rounds:\n            return \"Alright, I'm convinced. Let's proceed with your fix.\"\n\n        # Otherwise push back\n        self._pushback_count += 1\n        return self._generate_pushback(evidence, text)\n\n    # ------------------------------------------------------------------\n    # Evidence extraction\n    # ------------------------------------------------------------------\n    def _extract_evidence(self, test_results, lint_results, doc_results):\n        evidence = {\n            \"test_pass_ratio\": 0.0,\n            \"lint_errors\": 0,\n            \"doc_found\": False\n        }\n\n        # Parse test results\n        if test_results:\n            match = re.search(r'(\\d+)\\s*/\\s*(\\d+)', test_results)\n            if match:\n                p, t = int(match.group(1)), int(match.group(2))\n                evidence[\"test_pass_ratio\"] = p / t if t else 0.0\n            elif \"true\" in test_results.lower():\n                evidence[\"test_pass_ratio\"] = 1.0\n            elif \"false\" in test_results.lower():\n                evidence[\"test_pass_ratio\"] = 0.0\n\n        # Lint errors\n        if lint_results:\n            evidence[\"lint_errors\"] = len(re.findall(r'error', lint_results.lower()))\n\n        # Docs\n        if doc_results and \"no relevant\" not in doc_results.lower():\n            evidence[\"doc_found\"] = True\n\n        return evidence\n\n    # ------------------------------------------------------------------\n    # Explanation scoring\n    # ------------------------------------------------------------------\n    def _score_explanation(self, text: str) -> float:\n        score = 0.0\n\n        if \"because\" in text or \"therefore\" in text:\n            score += 0.3\n        if \"test\" in text or \"example\" in text:\n            score += 0.2\n        if len(text.split()) > 30:\n            score += 0.2\n        if \"error\" in text or \"fix\" in text:\n            score += 0.1\n\n        return min(1.0, score)\n\n    # ------------------------------------------------------------------\n    # Code inspection\n    # ------------------------------------------------------------------\n    def _inspect_code(self, new_code: str, old_code: str) -> float:\n        try:\n            t1 = ast.parse(old_code)\n            t2 = ast.parse(new_code)\n\n            n1 = len(list(ast.walk(t1)))\n            n2 = len(list(ast.walk(t2)))\n\n            change = abs(n2 - n1) / max(n1, 1)\n            return min(1.0, change)\n        except:\n            return 0.0\n\n    # ------------------------------------------------------------------\n    # Pushback generator\n    # ------------------------------------------------------------------\n    def _generate_pushback(self, evidence, text):\n        if evidence[\"test_pass_ratio\"] < 0.5:\n            return \"Tests are still failing. Show a passing case.\"\n\n        if evidence[\"lint_errors\"] > 0:\n            return f\"There are {evidence['lint_errors']} lint errors. Fix them.\"\n\n        if not evidence[\"doc_found\"]:\n            return \"Provide documentation or reference.\"\n\n        if \"because\" not in text:\n            return \"Explain why this works.\"\n\n        if len(text.split()) < 20:\n            return \"Too brief. Expand your reasoning.\"\n\n        return \"Not convinced yet. Give a concrete example.\"\n\n    # ------------------------------------------------------------------\n    # Score\n    # ------------------------------------------------------------------\n    def get_negotiation_score(self) -> float:\n        penalty = 0.1 * min(3, self._pushback_count)\n        return max(0.0, min(1.0, self._confidence - penalty))\n\n# models.py \u2013 Typed Models (Discriminated Unions, POMDP Separation)\nfrom typing import Literal, Union, Annotated, Optional\nfrom pydantic import BaseModel, Field, TypeAdapter, field_validator\n\n# ----------------------------------------------------------------------\n# Action classes (discriminated union)\n# ----------------------------------------------------------------------\nclass Action(BaseModel):\n    action_type: Literal[\"comment\", \"skip\", \"done\", \"question\",\n                         \"fix\", \"execute\", \"inspect\", \"run_linter\",\n                         \"run_tests\", \"query_docs\"]\n\nclass WriteComment(Action):\n    action_type: Literal[\"comment\"] = \"comment\"\n    comment_text: str = Field(..., min_length=1)\n\nclass Skip(Action):\n    action_type: Literal[\"skip\"] = \"skip\"\n\nclass Done(Action):\n    action_type: Literal[\"done\"] = \"done\"\n\nclass AskQuestion(Action):\n    action_type: Literal[\"question\"] = \"question\"\n    question: str = Field(..., min_length=1)\n\nclass ProposeFix(Action):\n    action_type: Literal[\"fix\"] = \"fix\"\n    fix_code: str = Field(..., min_length=1)\n    @field_validator('fix_code')\n    @classmethod\n    def not_empty(cls, v: str) -> str:\n        if not v.strip():\n            raise ValueError('fix_code cannot be empty')\n        return v\n\nclass Execute(Action):\n    action_type: Literal[\"execute\"] = \"execute\"\n\nclass Inspect(Action):\n    action_type: Literal[\"inspect\"] = \"inspect\"\n\nclass RunLinter(Action):\n    action_type: Literal[\"run_linter\"] = \"run_linter\"\n\nclass RunTests(Action):\n    action_type: Literal[\"run_tests\"] = \"run_tests\"\n\nclass QueryDocs(Action):\n    action_type: Literal[\"query_docs\"] = \"query_docs\"\n    query_topic: str = Field(..., min_length=1)\n\n# Discriminated union for one\u2011line polymorphic deserialization\nAnyAction = Annotated[\n    Union[WriteComment, Skip, Done, AskQuestion, ProposeFix,\n          Execute, Inspect, RunLinter, RunTests, QueryDocs],\n    Field(discriminator='action_type')\n]\naction_adapter = TypeAdapter(AnyAction)\n\n\ndef model_map_to_env(action_type: str, content: Optional[str] = None) -> AnyAction:\n    \"\"\"\n    Convert lightweight agent outputs into typed environment actions.\n    Kept at module level so training/inference code can reuse one mapping.\n    \"\"\"\n    if action_type == \"run_tests\":\n        return RunTests()\n    if action_type == \"run_linter\":\n        return RunLinter()\n    if action_type == \"inspect\":\n        return Inspect()\n    if action_type == \"fix\":\n        return ProposeFix(fix_code=content or \"\")\n    if action_type == \"comment\":\n        return WriteComment(comment_text=content or \"\")\n    if action_type == \"question\":\n        return AskQuestion(question=content or \"\")\n    if action_type == \"query_docs\":\n        return QueryDocs(query_topic=content or \"\")\n    if action_type == \"done\":\n        return Done()\n    return Skip()\n\n# ----------------------------------------------------------------------\n# Observation (POMDP \u2013 what the agent sees)\n# ----------------------------------------------------------------------\nclass Observation(BaseModel):\n    # Base schema model used by API metadata endpoints.\n    # Keep this lightweight for compatibility with legacy callers.\n    code_snippet: str\n    last_tool_output: str = \"\"\n    step: int = 0\n    done: bool = False\n\n# ----------------------------------------------------------------------\n# Reward (lightweight)\n# ----------------------------------------------------------------------\nclass Reward(BaseModel):\n    value: float\n\n# ----------------------------------------------------------------------\n# State (full environment state \u2013 not exposed to agent)\n# ----------------------------------------------------------------------\nclass State(BaseModel):\n    pr_title: str\n    pr_description: str\n    code_snippet: str\n    comments: list[str]\n    test_results: Optional[str]\n    step: int\n    done: bool\n\nimport json\nbug ={\n  \"easy\": {\n    \"null_check\": {\n      \"type\": \"ast\",\n      \"bug_type\": \"null_check\",\n      \"oracle_hint\": \"Add back the if-guard that was removed\"\n    },\n    \"simple_typo\": {\n      \"type\": \"ast\",\n      \"bug_type\": \"simple_typo\",\n      \"oracle_hint\": \"Fix the misspelled variable name\"\n    },\n    \"string_index\": {\n      \"type\": \"ast\",\n      \"bug_type\": \"string_index\",\n      \"oracle_hint\": \"Correct the index offset\"\n    },\n    \"default_value\": {\n      \"type\": \"ast\",\n      \"bug_type\": \"default_value\",\n      \"oracle_hint\": \"Restore dict.get() with proper default\"\n    },\n    \"empty_return\": {\n      \"type\": \"ast\",\n      \"bug_type\": \"empty_return\",\n      \"oracle_hint\": \"Remove the premature return None\"\n    }\n  },\n  \"medium\": {\n    \"off_by_one\": {\n      \"type\": \"ast\",\n      \"bug_type\": \"off_by_one\"\n    },\n    \"loop_skip\": {\n      \"type\": \"ast\",\n      \"bug_type\": \"loop_skip\"\n    },\n    \"sign_error\": {\n      \"type\": \"ast\",\n      \"bug_type\": \"sign_error\"\n    },\n    \"swap_args\": {\n      \"type\": \"ast\",\n      \"bug_type\": \"swap_args\"\n    },\n    \"uninitialised_var\": {\n      \"type\": \"ast\",\n      \"bug_type\": \"uninitialised_var\"\n    }\n  },\n  \"hard\": {\n    \"division_by_zero_empty\": {\n      \"type\": \"ast\",\n      \"bug_type\": \"division_by_zero_empty\"\n    },\n    \"division_by_zero_zero\": {\n      \"type\": \"ast\",\n      \"bug_type\": \"division_by_zero_zero\"\n    },\n    \"float_precision\": {\n      \"type\": \"ast\",\n      \"bug_type\": \"float_precision\"\n    },\n    \"abs_usage\": {\n      \"type\": \"ast\",\n      \"bug_type\": \"abs_usage\"\n    },\n    \"round_error\": {\n      \"type\": \"ast\",\n      \"bug_type\": \"round_error\"\n    }\n  },\n  \"harder\": {\n    \"missing_lock\": {\n      \"type\": \"template\",\n      \"buggy\": \"counter = 0\\ndef increment():\\n    global counter\\n    counter += 1\",\n      \"oracle\": \"counter = 0\\nimport threading\\nlock = threading.Lock()\\ndef increment():\\n    global counter\\n    with lock:\\n        counter += 1\"\n    },\n    \"double_lock\": {\n      \"type\": \"template\",\n      \"buggy\": \"import threading\\nlock = threading.Lock()\\ndef do_work():\\n    lock.acquire()\\n    lock.acquire()\\n    print('working')\\n    lock.release()\",\n      \"oracle\": \"import threading\\nlock = threading.Lock()\\ndef do_work():\\n    with lock:\\n        print('working')\"\n    },\n    \"global_nonatomic\": {\n      \"type\": \"template\",\n      \"buggy\": \"count = 0\\ndef add():\\n    global count\\n    count = count + 1\",\n      \"oracle\": \"count = 0\\ndef add():\\n    global count\\n    count += 1\"\n    },\n    \"thread_safe_list\": {\n      \"type\": \"template\",\n      \"buggy\": \"import threading\\nitems = []\\ndef append_item(item):\\n    items.append(item)\",\n      \"oracle\": \"import threading\\nitems = []\\nlock = threading.Lock()\\ndef append_item(item):\\n    with lock:\\n        items.append(item)\"\n    },\n    \"volatile_read\": {\n      \"type\": \"template\",\n      \"buggy\": \"import threading\\nstop = False\\ndef worker():\\n    while not stop:\\n        pass\",\n      \"oracle\": \"import threading\\nstop = False\\nlock = threading.Lock()\\ndef worker():\\n    while True:\\n        with lock:\\n            if stop:\\n                break\"\n    }\n  },\n  \"hardest\": {\n    \"deadlock_order\": {\n      \"type\": \"template\",\n      \"buggy\": \"import threading\\nlock1 = threading.Lock()\\nlock2 = threading.Lock()\\ndef thread1():\\n    with lock1:\\n        with lock2:\\n            pass\\ndef thread2():\\n    with lock2:\\n        with lock1:\\n            pass\",\n      \"oracle\": \"import threading\\nlock1 = threading.Lock()\\nlock2 = threading.Lock()\\ndef thread1():\\n    with lock1:\\n        with lock2:\\n            pass\\ndef thread2():\\n    with lock1:\\n        with lock2:\\n            pass\"\n    },\n    \"nested_lock_timeout\": {\n      \"type\": \"template\",\n      \"buggy\": \"import threading\\nlock = threading.Lock()\\ndef work():\\n    lock.acquire()\\n    # critical section\\n    lock.release()\",\n      \"oracle\": \"import threading\\nlock = threading.Lock()\\ndef work():\\n    if lock.acquire(timeout=1):\\n        try:\\n            # critical section\\n        finally:\\n            lock.release()\"\n    },\n    \"fork_join\": {\n      \"type\": \"template\",\n      \"buggy\": \"import threading\\ndef worker():\\n    pass\\nt = threading.Thread(target=worker)\\nt.start()\",\n      \"oracle\": \"import threading\\ndef worker():\\n    pass\\nt = threading.Thread(target=worker)\\nt.start()\\nt.join()\"\n    },\n    \"mutex_release\": {\n      \"type\": \"template\",\n      \"buggy\": \"import threading\\nlock = threading.Lock()\\ndef thread_A():\\n    lock.acquire()\\n    lock.release()\\ndef thread_B():\\n    lock.release()\",\n      \"oracle\": \"import threading\\nlock = threading.Lock()\\ndef thread_A():\\n    with lock:\\n        pass\\ndef thread_B():\\n    with lock:\\n        pass\"\n    },\n    \"race_on_init\": {\n      \"type\": \"template\",\n      \"buggy\": \"import threading\\nitems = []\\ndef init():\\n    global items\\n    items = [1,2,3]\\nt = threading.Thread(target=init)\\nt.start()\\nprint(items)\",\n      \"oracle\": \"import threading\\nitems = []\\ndef init():\\n    global items\\n    items = [1,2,3]\\nt = threading.Thread(target=init)\\nt.start()\\nt.join()\\nprint(items)\"\n    }\n  }\n}\n\nwith open(\"bug.json\", \"w\") as f:\n    json.dump(bug, f, indent=2)\n\nprint(\"Created bug.json\")\n\n# redteam.py \u2013 Task\u2011aware bug injection (25 bugs, 5 difficulty levels)\nimport ast\nimport random\nfrom dataclasses import dataclass, field\nfrom typing import Tuple, Optional, List, Dict\n\n# ----------------------------------------------------------------------\n# 1. AST Bug Injector (extended for all simple bugs)\n# ----------------------------------------------------------------------\nclass ASTBugInjector(ast.NodeTransformer):\n    def __init__(self, bug_type: str):\n        super().__init__()\n        self.bug_type = bug_type\n        self.modified = False\n\n    # --- Easy: null_check, simple_typo, string_index, default_value, empty_return ---\n    def visit_If(self, node: ast.If):\n        # null_check: remove the if-guard\n        if self.bug_type == \"null_check\" and not self.modified:\n            if node.body and len(node.body) == 1:\n                self.modified = True\n                return node.body[0]\n        # division_by_zero_empty: remove the empty check\n        if self.bug_type == \"division_by_zero_empty\" and not self.modified:\n            # pattern: if not data: return 0  \u2013 we delete the entire if\n            if (isinstance(node.test, ast.UnaryOp) and\n                isinstance(node.test.op, ast.Not) and\n                isinstance(node.test.operand, ast.Name)):\n                self.modified = True\n                return None  # signal to remove this node from parent\n        return self.generic_visit(node)\n\n    def visit_Name(self, node: ast.Name):\n        if self.bug_type == \"simple_typo\" and not self.modified:\n            if node.id == \"users\":\n                self.modified = True\n                return ast.Name(id=\"usres\", ctx=node.ctx)\n        return self.generic_visit(node)\n\n    def visit_Subscript(self, node: ast.Subscript):\n        if self.bug_type == \"string_index\" and not self.modified:\n            if isinstance(node.slice, ast.Index) and isinstance(node.slice.value, ast.Constant):\n                old_val = node.slice.value.value\n                if isinstance(old_val, int):\n                    self.modified = True\n                    node.slice = ast.Index(value=ast.Constant(value=old_val + 1))\n        return self.generic_visit(node)\n\n    def visit_Call(self, node: ast.Call):\n        # default_value: change dict.get(key) to dict[key] (no default)\n        if self.bug_type == \"default_value\" and not self.modified:\n            if (isinstance(node.func, ast.Attribute) and\n                node.func.attr == \"get\" and len(node.args) == 1):\n                self.modified = True\n                return ast.Subscript(\n                    value=node.func.value,\n                    slice=ast.Index(value=node.args[0]),\n                    ctx=node.ctx\n                )\n        # abs_usage: remove abs()\n        if self.bug_type == \"abs_usage\" and not self.modified:\n            if isinstance(node.func, ast.Name) and node.func.id == \"abs\":\n                self.modified = True\n                return node.args[0]\n        return self.generic_visit(node)\n\n    def visit_FunctionDef(self, node: ast.FunctionDef):\n        # empty_return: insert a premature return None\n        if self.bug_type == \"empty_return\" and not self.modified:\n            self.modified = True\n            node.body.insert(0, ast.Return(value=ast.Constant(value=None)))\n        return self.generic_visit(node)\n\n    # --- Medium: off_by_one, loop_skip, sign_error, swap_args, uninitialised_var ---\n    def visit_For(self, node: ast.For):\n        if (self.bug_type in (\"off_by_one\", \"loop_skip\")) and not self.modified:\n            if (isinstance(node.iter, ast.Call) and\n                isinstance(node.iter.func, ast.Name) and\n                node.iter.func.id == \"range\"):\n                if self.bug_type == \"off_by_one\":\n                    new_iter = ast.Call(\n                        func=ast.Name(id='range', ctx=ast.Load()),\n                        args=[\n                            ast.Constant(value=1),\n                            ast.BinOp(left=node.iter.args[0], op=ast.Sub(), right=ast.Constant(value=1))\n                        ],\n                        keywords=[]\n                    )\n                    node.iter = new_iter\n                    self.modified = True\n                elif self.bug_type == \"loop_skip\" and len(node.iter.args) == 1:\n                    new_iter = ast.Call(\n                        func=ast.Name(id='range', ctx=ast.Load()),\n                        args=[ast.BinOp(left=node.iter.args[0], op=ast.Sub(), right=ast.Constant(value=1))],\n                        keywords=[]\n                    )\n                    node.iter = new_iter\n                    self.modified = True\n        return self.generic_visit(node)\n\n    def visit_BinOp(self, node: ast.BinOp):\n        # sign_error: flip Add/Sub, wrong_operator: Add->Sub, float_precision: Div->FloorDiv\n        if not self.modified:\n            if self.bug_type in (\"wrong_operator\", \"sign_error\"):\n                if isinstance(node.op, ast.Add):\n                    node.op = ast.Sub()\n                    self.modified = True\n                elif isinstance(node.op, ast.Sub):\n                    node.op = ast.Add()\n                    self.modified = True\n            elif self.bug_type == \"float_precision\" and isinstance(node.op, ast.Div):\n                node.op = ast.FloorDiv()\n                self.modified = True\n        return self.generic_visit(node)\n\n    def visit_arguments(self, node: ast.arguments):\n        # swap_args: swap first two arguments of a function\n        if self.bug_type == \"swap_args\" and not self.modified and len(node.args) >= 2:\n            self.modified = True\n            node.args[0], node.args[1] = node.args[1], node.args[0]\n        return self.generic_visit(node)\n\n    def visit_Assign(self, node: ast.Assign):\n        # uninitialised_var: remove an assignment statement (replaced with Pass)\n        if self.bug_type == \"uninitialised_var\" and not self.modified:\n            self.modified = True\n            return ast.Pass()\n        return self.generic_visit(node)\n\n# ----------------------------------------------------------------------\n# 2. Bug database (25 bugs, categorized by difficulty)\n# ----------------------------------------------------------------------\nBUG_DB = {\n    \"easy\": {\n        \"null_check\":    {\"type\": \"ast\", \"bug_type\": \"null_check\"},\n        \"simple_typo\":   {\"type\": \"ast\", \"bug_type\": \"simple_typo\"},\n        \"string_index\":  {\"type\": \"ast\", \"bug_type\": \"string_index\"},\n        \"default_value\": {\"type\": \"ast\", \"bug_type\": \"default_value\"},\n        \"empty_return\":  {\"type\": \"ast\", \"bug_type\": \"empty_return\"},\n    },\n    \"medium\": {\n        \"off_by_one\":     {\"type\": \"ast\", \"bug_type\": \"off_by_one\"},\n        \"loop_skip\":      {\"type\": \"ast\", \"bug_type\": \"loop_skip\"},\n        \"sign_error\":     {\"type\": \"ast\", \"bug_type\": \"sign_error\"},\n        \"swap_args\":      {\"type\": \"ast\", \"bug_type\": \"swap_args\"},\n        \"uninitialised_var\": {\"type\": \"ast\", \"bug_type\": \"uninitialised_var\"},\n    },\n    \"hard\": {\n        \"division_by_zero_empty\": {\"type\": \"ast\", \"bug_type\": \"division_by_zero_empty\"},\n        \"division_by_zero_zero\":  {\"type\": \"ast\", \"bug_type\": \"division_by_zero_empty\"},  # same injector\n        \"float_precision\":        {\"type\": \"ast\", \"bug_type\": \"float_precision\"},\n        \"abs_usage\":              {\"type\": \"ast\", \"bug_type\": \"abs_usage\"},\n        \"round_error\":            {\"type\": \"ast\", \"bug_type\": \"round_error\"},  # can be extended\n    },\n    \"harder\": {\n        \"missing_lock\": {\n            \"type\": \"template\",\n            \"buggy\": \"counter = 0\\ndef increment():\\n    global counter\\n    counter += 1\",\n            \"oracle\": \"counter = 0\\nimport threading\\nlock = threading.Lock()\\ndef increment():\\n    global counter\\n    with lock:\\n        counter += 1\",\n        },\n        \"double_lock\": {\n            \"type\": \"template\",\n            \"buggy\": \"import threading\\nlock = threading.Lock()\\ndef do_work():\\n    lock.acquire()\\n    lock.acquire()\\n    print('working')\\n    lock.release()\",\n            \"oracle\": \"import threading\\nlock = threading.Lock()\\ndef do_work():\\n    with lock:\\n        print('working')\",\n        },\n        \"global_nonatomic\": {\n            \"type\": \"template\",\n            \"buggy\": \"count = 0\\ndef add():\\n    global count\\n    count = count + 1\",\n            \"oracle\": \"count = 0\\ndef add():\\n    global count\\n    count += 1\",\n        },\n        \"thread_safe_list\": {\n            \"type\": \"template\",\n            \"buggy\": \"import threading\\nitems = []\\ndef append_item(item):\\n    items.append(item)\",\n            \"oracle\": \"import threading\\nitems = []\\nlock = threading.Lock()\\ndef append_item(item):\\n    with lock:\\n        items.append(item)\",\n        },\n        \"volatile_read\": {\n            \"type\": \"template\",\n            \"buggy\": \"import threading\\nstop = False\\ndef worker():\\n    while not stop:\\n        pass\",\n            \"oracle\": \"import threading\\nstop = False\\nlock = threading.Lock()\\ndef worker():\\n    while True:\\n        with lock:\\n            if stop:\\n                break\",\n        },\n    },\n    \"hardest\": {\n        \"deadlock_order\": {\n            \"type\": \"template\",\n            \"buggy\": \"import threading\\nlock1 = threading.Lock()\\nlock2 = threading.Lock()\\ndef thread1():\\n    with lock1:\\n        with lock2:\\n            pass\\ndef thread2():\\n    with lock2:\\n        with lock1:\\n            pass\",\n            \"oracle\": \"import threading\\nlock1 = threading.Lock()\\nlock2 = threading.Lock()\\ndef thread1():\\n    with lock1:\\n        with lock2:\\n            pass\\ndef thread2():\\n    with lock1:\\n        with lock2:\\n            pass\",\n        },\n        \"nested_lock_timeout\": {\n            \"type\": \"template\",\n            \"buggy\": \"import threading\\nlock = threading.Lock()\\ndef work():\\n    lock.acquire()\\n    # critical section\\n    lock.release()\",\n            \"oracle\": \"import threading\\nlock = threading.Lock()\\ndef work():\\n    if lock.acquire(timeout=1):\\n        try:\\n            # critical section\\n        finally:\\n            lock.release()\",\n        },\n        \"fork_join\": {\n            \"type\": \"template\",\n            \"buggy\": \"import threading\\ndef worker():\\n    pass\\nt = threading.Thread(target=worker)\\nt.start()\",\n            \"oracle\": \"import threading\\ndef worker():\\n    pass\\nt = threading.Thread(target=worker)\\nt.start()\\nt.join()\",\n        },\n        \"mutex_release\": {\n            \"type\": \"template\",\n            \"buggy\": \"import threading\\nlock = threading.Lock()\\ndef thread_A():\\n    lock.acquire()\\n    lock.release()\\ndef thread_B():\\n    lock.release()\",\n            \"oracle\": \"import threading\\nlock = threading.Lock()\\ndef thread_A():\\n    with lock:\\n        pass\\ndef thread_B():\\n    with lock:\\n        pass\",\n        },\n        \"race_on_init\": {\n            \"type\": \"template\",\n            \"buggy\": \"import threading\\nitems = []\\ndef init():\\n    global items\\n    items = [1,2,3]\\nt = threading.Thread(target=init)\\nt.start()\\nprint(items)\",\n            \"oracle\": \"import threading\\nitems = []\\ndef init():\\n    global items\\n    items = [1,2,3]\\nt = threading.Thread(target=init)\\nt.start()\\nt.join()\\nprint(items)\",\n        },\n    },\n}\n\n# ----------------------------------------------------------------------\n# 3. Derived helpers\n# ----------------------------------------------------------------------\nTASK_BUG_MAP = {level: list(bugs.keys()) for level, bugs in BUG_DB.items()}\n\nTEMPLATE_BUGS = {}\nfor level, bugs in BUG_DB.items():\n    for bug_id, bug in bugs.items():\n        if bug[\"type\"] == \"template\":\n            TEMPLATE_BUGS[bug_id] = (bug[\"buggy\"], bug[\"oracle\"])\n\n# ----------------------------------------------------------------------\n# 4. RedTeam Controller (task\u2011aware)\n# ----------------------------------------------------------------------\n@dataclass\nclass RedTeam:\n    task: str\n    seed: Optional[int] = 42\n    noise_prob: float = 0.2\n    _random: random.Random = field(init=False)\n\n    def __post_init__(self):\n        self._random = random.Random(self.seed)\n\n    def inject_bug(self, original_code: str) -> Tuple[str, str, str, str]:\n        \"\"\"\n        Returns: (buggy_code, bug_type, description, oracle_fix)\n        Selects a bug appropriate for the task difficulty.\n        \"\"\"\n        bug_list = TASK_BUG_MAP.get(self.task, [\"null_check\"])\n        bug_type = self._random.choice(bug_list)\n\n        # Template bug: return hardcoded buggy + oracle\n        if bug_type in TEMPLATE_BUGS:\n            buggy_code, oracle_code = TEMPLATE_BUGS[bug_type]\n            description = f\"Template bug: {bug_type}\"\n            if self._random.random() < self.noise_prob:\n                buggy_code += \"\\n# TODO: refactor later\"\n            return buggy_code, bug_type, description, oracle_code\n\n        # AST injection\n        try:\n            tree = ast.parse(original_code)\n        except SyntaxError:\n            return original_code, \"parse_error\", \"Syntax error in original code\", original_code\n\n        injector = ASTBugInjector(bug_type)\n        modified_tree = injector.visit(tree)\n        ast.fix_missing_locations(modified_tree)\n\n        if injector.modified:\n            buggy_code = ast.unparse(modified_tree)\n            oracle_fix = original_code\n            description = f\"AST bug: {bug_type}\"\n        else:\n            buggy_code = original_code\n            oracle_fix = original_code\n            bug_type = \"no_op\"\n            description = \"No suitable code structure found for injection\"\n\n        if self._random.random() < self.noise_prob:\n            buggy_code += \"\\n# TODO: refactor later\"\n\n        return buggy_code, bug_type, description, oracle_fix\n\n# tools.py \u2013 Real vector retrieval for query_docs, linter, and test runner\nimport subprocess\nimport tempfile\nimport os\nfrom dataclasses import dataclass\nfrom sentence_transformers import SentenceTransformer\nimport chromadb\n\n@dataclass\nclass ToolBox:\n    _embedder = None\n    _client = None\n    _collection = None\n\n    @classmethod\n    def _get_embedder(cls):\n        if cls._embedder is None:\n            cls._embedder = SentenceTransformer('all-MiniLM-L6-v2')\n        return cls._embedder\n\n    @classmethod\n    def _get_collection(cls):\n        if cls._collection is None:\n            cls._client = chromadb.Client()\n            cls._collection = cls._client.create_collection(\"docs\")\n            # Pre\u2011load real documentation snippets (can be extended)\n            docs = [\n                \"KeyError occurs when a dictionary key is missing. Use dict.get() or check 'if key in dict'.\",\n                \"pylint error C0304: missing final newline. Add a newline at the end of file.\",\n                \"Deadlock happens when two threads acquire locks in opposite order. Always acquire locks in the same order.\",\n                \"Division by zero: check if list is empty before calculating average, or use try/except.\",\n                \"Threading.Lock: use 'with lock:' to automatically acquire and release.\",\n                \"Off\u2011by\u2011one errors: adjust loop ranges, e.g., range(1, len(arr)-1).\",\n            ]\n            embedder = cls._get_embedder()\n            embeddings = embedder.encode(docs).tolist()\n            for i, doc in enumerate(docs):\n                cls._collection.add(ids=[str(i)], documents=[doc], embeddings=[embeddings[i]])\n        return cls._collection\n\n    @staticmethod\n    def run_linter(code: str) -> str:\n        with tempfile.NamedTemporaryFile(mode='w', suffix='.py', delete=False, encoding='utf-8') as f:\n            f.write(code)\n            f.flush()\n            tmp_path = f.name\n        try:\n            result = subprocess.run(\n                ['pylint', tmp_path, '--exit-zero', '--output-format=text'],\n                capture_output=True,\n                text=True,\n                timeout=10,\n                encoding='utf-8'\n            )\n            output = result.stdout\n            if \"Your code has been rated\" in output:\n                output = output.split(\"Your code has been rated\")[0]\n            output = output.strip()\n            if not output:\n                return \"No linting issues found.\"\n            return output[:500]\n        except FileNotFoundError:\n            return \"Linter (pylint) not installed.\"\n        except subprocess.TimeoutExpired:\n            return \"Linter timed out.\"\n        except Exception as e:\n            return f\"Linter error: {str(e)}\"\n        finally:\n            try:\n                os.unlink(tmp_path)\n            except:\n                pass\n\n    @staticmethod\n    def run_tests(test_script: str) -> str:\n        with tempfile.NamedTemporaryFile(mode='w', suffix='.py', delete=False, encoding='utf-8') as f:\n            f.write(test_script)\n            f.flush()\n            tmp_path = f.name\n        try:\n            result = subprocess.run(\n                ['python', tmp_path],\n                capture_output=True,\n                text=True,\n                timeout=10,\n                encoding='utf-8'\n            )\n            output = result.stdout + result.stderr\n            return output.strip() or \"Test executed successfully (no output).\"\n        except subprocess.TimeoutExpired:\n            return \"Test execution timed out.\"\n        except Exception as e:\n            return f\"Test runner error: {str(e)}\"\n        finally:\n            try:\n                os.unlink(tmp_path)\n            except:\n                pass\n\n    @classmethod\n    def query_docs(cls, topic: str) -> str:\n        \"\"\"Retrieve top 3 relevant docs. Forces agent to reason across multiple hints.\"\"\"\n        try:\n            embedder = cls._get_embedder()\n            collection = cls._get_collection()\n            query_emb = embedder.encode([topic]).tolist()\n            # Get top 3 results (not just 1)\n            results = collection.query(query_embeddings=query_emb, n_results=3)\n            if results['documents'] and results['documents'][0]:\n                # Return concatenated snippets, labelled for clarity\n                snippets = []\n                for i, doc in enumerate(results['documents'][0]):\n                    snippets.append(f\"[{i+1}] {doc}\")\n                return \"Relevant documentation:\\n\" + \"\\n\".join(snippets)\n            return \"No relevant documentation found.\"\n        except Exception:\n            # Fallback to keyword matching\n            topic_lower = topic.lower()\n            fallback = {\n                \"null check\": \"To avoid KeyError, use 'if key in dict:' before accessing.\",\n                \"keyerror\": \"Catch KeyError with try/except or use dict.get().\",\n                \"deadlock\": \"Always acquire locks in the same order to avoid deadlock.\",\n            }\n            for key, value in fallback.items():\n                if key in topic_lower:\n                    return value\n            return \"No relevant documentation found. Try being more specific.\"\n\n# test_runner.py \u2013 Full production version with continuous scoring, dynamic function detection, and randomised tests\nimport subprocess\nimport tempfile\nimport os\nimport json\nimport ast\nimport random\nimport sys\nfrom typing import Tuple, List, Any, Optional\nfrom dataclasses import dataclass\n\n# Bridge fine-grained RedTeam ids to canonical TestRunner families.\n# This keeps evaluation stable even when bug generators use richer labels.\nBUG_ID_CANONICAL_MAP = {\n    # Easy-family bugs on `get_user`-style behavior.\n    \"simple_typo\": \"null_check\",\n    \"default_value\": \"null_check\",\n    \"empty_return\": \"null_check\",\n    \"string_index\": \"off_by_one\",\n\n    # Medium arithmetic/control-flow aliases.\n    \"loop_skip\": \"off_by_one\",\n    \"sign_error\": \"wrong_operator\",\n    \"swap_args\": \"wrong_operator\",\n    \"uninitialised_var\": \"null_check\",\n\n    # Hard numeric-safety aliases.\n    \"division_by_zero_empty\": \"division_by_zero\",\n    \"division_by_zero_zero\": \"division_by_zero\",\n    \"float_precision\": \"division_by_zero\",\n    \"abs_usage\": \"division_by_zero\",\n    \"round_error\": \"division_by_zero\",\n}\n\n@dataclass\nclass TestRunner:\n    bug_id: str\n    timeout_sec: int = 5\n    max_memory_mb: int = 256\n    fuzz_rounds: int = 3   # number of random test cases per bug\n\n    def run_tests(self, fix_code: str) -> Tuple[float, str]:\n        \"\"\"\n        Returns (score, output_message) where score is proportion of passed tests (0.0\u20131.0).\n        \"\"\"\n        # 1. Detect the function defined in the agent's code (dynamic)\n        func_name = self._get_defined_function_name(fix_code)\n        if not func_name:\n            return 0.0, \"No function definition found in agent code\"\n\n        # 2. Normalize bug id so broader RedTeam ids still hit meaningful tests.\n        canonical_bug_id = self._canonical_bug_id()\n\n        # 3. Generate the test script (includes fixed + fuzzed test cases)\n        test_script = self._generate_test_script(fix_code, func_name, canonical_bug_id)\n        with tempfile.NamedTemporaryFile(mode='w', suffix='.py', delete=False, encoding='utf-8') as f:\n            f.write(test_script)\n            tmp_path = f.name\n\n        try:\n            # Resource limiting (Linux only; fallback otherwise)\n            try:\n                import resource\n                resource.setrlimit(resource.RLIMIT_AS, (self.max_memory_mb * 1024 * 1024, self.max_memory_mb * 1024 * 1024))\n            except Exception:\n                pass\n\n            result = subprocess.run(\n                [sys.executable, tmp_path],\n                capture_output=True,\n                text=True,\n                timeout=self.timeout_sec,\n                encoding='utf-8'\n            )\n            # Parse JSON output\n            try:\n                data = json.loads(result.stdout.strip())\n                passed = data.get(\"passed\", 0)\n                total = data.get(\"total\", 1)\n                score = passed / total if total > 0 else 0.0\n                return score, result.stdout.strip()\n            except json.JSONDecodeError:\n                # Fallback: look for \"True\" (legacy)\n                if \"True\" in result.stdout:\n                    return 1.0, result.stdout\n                return 0.0, result.stdout\n        except subprocess.TimeoutExpired:\n            return 0.0, \"Test execution timed out\"\n        except Exception as e:\n            return 0.0, f\"Unexpected error: {str(e)}\"\n        finally:\n            try:\n                os.unlink(tmp_path)\n            except:\n                pass\n\n    def _get_defined_function_name(self, code: str) -> Optional[str]:\n        \"\"\"Extract the target function name from the code.\n           Looks for a function named 'fix' first; otherwise returns the first function found.\n        \"\"\"\n        try:\n            tree = ast.parse(code)\n            first_func = None\n            for node in ast.walk(tree):\n                if isinstance(node, ast.FunctionDef):\n                    if node.name == \"fix\":\n                        return \"fix\"\n                    if first_func is None:\n                        first_func = node.name\n            return first_func   # fallback if no 'fix' function exists\n        except SyntaxError:\n            pass\n        return None\n\n    def _canonical_bug_id(self) -> str:\n        \"\"\"Return canonical bug family used by this test harness.\"\"\"\n        return BUG_ID_CANONICAL_MAP.get(self.bug_id, self.bug_id)\n\n    def _generate_test_script(self, fix_code: str, func_name: str, canonical_bug_id: str) -> str:\n        \"\"\"Generate a test script that runs fixed + fuzzed test cases and outputs JSON.\"\"\"\n        test_cases = self._get_test_cases(canonical_bug_id, func_name)\n        fuzzed_cases = self._generate_fuzzed_cases(canonical_bug_id, func_name)\n        all_cases = test_cases + fuzzed_cases\n\n        lines = []\n        lines.append(fix_code)\n        lines.append(\"\")\n        lines.append(\"import json\")\n        lines.append(\"\")\n        lines.append(\"def run_tests():\")\n        lines.append(f\"    test_cases = {json.dumps(all_cases)}\")\n        lines.append(\"    passed = 0\")\n        lines.append(\"    total = len(test_cases)\")\n        lines.append(\"    for args, expected in test_cases:\")\n        lines.append(f\"        try:\")\n        lines.append(f\"            result = {func_name}(*args) if isinstance(args, list) else {func_name}(args)\")\n        lines.append(f\"            if result == expected:\")\n        lines.append(f\"                passed += 1\")\n        lines.append(f\"        except Exception:\")\n        lines.append(f\"            pass\")\n        lines.append(\"    return {'passed': passed, 'total': total}\")\n        lines.append(\"\")\n        lines.append(\"if __name__ == '__main__':\")\n        lines.append(\"    result = run_tests()\")\n        lines.append(\"    print(json.dumps(result))\")\n        return \"\\n\".join(lines)\n\n    def _get_test_cases(self, canonical_bug_id: str, func_name: str) -> List[Tuple[List[Any], Any]]:\n        \"\"\"\n        Return a list of (arguments, expected_output) for the given bug_id.\n        Uses the actual function name (dynamic) for consistency.\n        \"\"\"\n        if canonical_bug_id == \"null_check\":\n            return [\n                ([{\"users\": {\"alice\": \"Alice\"}, \"id\": \"bob\"}], None),   # missing key should not crash\n                ([{\"users\": {\"alice\": \"Alice\"}, \"id\": \"alice\"}], \"Alice\"),\n            ]\n        elif canonical_bug_id == \"off_by_one\":\n            return [\n                ([[1,2,3,4]], 4),\n                ([[]], 0),\n            ]\n        elif canonical_bug_id == \"division_by_zero\":\n            return [\n                ([[]], 0),\n                ([[1,2,3]], 2.0),\n            ]\n        elif canonical_bug_id == \"wrong_operator\":\n            return [\n                ([5,3], 8),\n                ([-1,1], 0),\n            ]\n        else:\n            # For missing_lock, deadlock_order, etc., return empty list (will be handled gracefully)\n            return []\n\n    def _generate_fuzzed_cases(self, canonical_bug_id: str, func_name: str) -> List[Tuple[List[Any], Any]]:\n        \"\"\"\n        Generate random test cases to prevent memorisation.\n        Only for bugs where meaningful fuzzing is possible.\n        \"\"\"\n        cases = []\n        if canonical_bug_id == \"null_check\":\n            # Random users dictionary and random ids\n            for _ in range(self.fuzz_rounds):\n                users = {f\"user_{i}\": f\"name_{i}\" for i in range(random.randint(1, 5))}\n                # Pick existing or missing key\n                if random.random() > 0.5:\n                    key = random.choice(list(users.keys()))\n                    expected = users[key]\n                else:\n                    key = \"missing_\" + str(random.randint(100, 999))\n                    expected = None\n                cases.append(([{\"users\": users, \"id\": key}], expected))\n        elif canonical_bug_id == \"off_by_one\":\n            for _ in range(self.fuzz_rounds):\n                length = random.randint(0, 10)\n                arr = list(range(length))\n                cases.append(([arr], length))\n        elif canonical_bug_id == \"division_by_zero\":\n            for _ in range(self.fuzz_rounds):\n                length = random.randint(0, 10)\n                data = [random.randint(-100, 100) for _ in range(length)]\n                expected = sum(data)/length if length else 0\n                cases.append(([data], expected))\n        elif canonical_bug_id == \"wrong_operator\":\n            for _ in range(self.fuzz_rounds):\n                a = random.randint(-100, 100)\n                b = random.randint(-100, 100)\n                cases.append(([a, b], a + b))\n        return cases\n\n# rubrics.py \u2013 Self-contained Rubrics (no external OpenEnv dependency)\n\nclass Rubric:\n    \"\"\"Minimal Rubric base \u2013 compatible with OpenEnv but self\u2011contained.\"\"\"\n    def __call__(self, env, action, obs, reward, done, info):\n        return 0.0\n\n\n# --------------------------------------------------------------------------------\n# 1. TOOL\u2011USAGE BONUS\n# --------------------------------------------------------------------------------\nclass ToolUsageRubric(Rubric):\n    def __init__(self, bonus: float = 0.05):\n        self.bonus = bonus\n\n    def __call__(self, env, action, obs, reward, done, info):\n        score = 0.0\n        action_type = info.get(\"action_type\", \"\")\n        # Use pre-action flags from `info` so first-use bonuses are\n        # computed correctly even though env flags are mutated in-step.\n        prev_tests_run = info.get(\"prev_tests_run\", env._tests_run)\n        prev_linter_run = info.get(\"prev_linter_run\", env._linter_run)\n        prev_docs_queried = info.get(\"prev_docs_queried\", env._docs_queried)\n\n        if action_type == \"run_tests\":\n            if not prev_tests_run:\n                score += self.bonus\n            score += 0.015\n        elif action_type == \"run_linter\":\n            if not prev_linter_run:\n                score += self.bonus\n            score += 0.015\n        elif action_type == \"query_docs\":\n            if not prev_docs_queried:\n                score += self.bonus * 0.5\n            # Encourage docs usage when it is likely useful:\n            # - early exploration phase\n            # - non-trivial query text\n            if env._step_count <= 4 and info.get(\"docs_query_len\", 0) >= 8:\n                score += 0.01\n            # Discourage repeated docs calls after the first-use signal.\n            if prev_docs_queried:\n                score -= 0.01\n        elif action_type == \"question\" and env._step_count <= 3:\n            score += 0.02\n        return score\n\n\n# --------------------------------------------------------------------------------\n# 2. DELTA\u2011BASED REWARDS\n# --------------------------------------------------------------------------------\nclass TestDeltaRubric(Rubric):\n    def __init__(self, weight: float = 0.3):\n        self.weight = weight\n\n    def __call__(self, env, action, obs, reward, done, info):\n        delta = env._current_test_score - env._previous_test_score\n        effective = self.weight\n        if info.get(\"action_type\") == \"fix\":\n            effective *= 0.4\n        return effective * delta\n\n\nclass LintDeltaRubric(Rubric):\n    def __init__(self, weight: float = 0.3):\n        self.weight = weight\n\n    def __call__(self, env, action, obs, reward, done, info):\n        delta = env._current_lint_score - env._previous_lint_score\n        effective = self.weight * 0.5\n        if info.get(\"action_type\") == \"fix\":\n            effective *= 0.4\n        return effective * delta\n\n\n# --------------------------------------------------------------------------------\n# 3. TERMINAL SUCCESS BONUS\n# --------------------------------------------------------------------------------\nclass TerminalSuccessRubric(Rubric):\n    def __call__(self, env, action, obs, reward, done, info):\n        if info.get(\"action_type\") != \"fix\":\n            return 0.0\n        score = 0.0\n        if env._current_test_score > 0.95:\n            score += 0.4\n        elif env._current_test_score > 0.85:\n            score += 0.2\n        return score\n\n\n# --------------------------------------------------------------------------------\n# 4. EXPLORATION & DIVERSITY\n# --------------------------------------------------------------------------------\nclass ExplorationRubric(Rubric):\n    def __init__(self, penalty: float = -0.05, bonus: float = 0.021):\n        self.penalty = penalty\n        self.bonus = bonus\n\n    def __call__(self, env, action, obs, reward, done, info):\n        if len(env._action_history) < 3:\n            return 0.0\n        recent = env._action_history[-3:]\n        unique = len(set(recent))\n        if unique == 1:\n            return self.penalty\n        elif unique == 3:\n            return self.bonus\n        return 0.0\n\n\n# --------------------------------------------------------------------------------\n# 5. ANTI\u2011HACKING & CONSISTENCY\n# --------------------------------------------------------------------------------\nclass AntiHackingRubric(Rubric):\n    def __call__(self, env, action, obs, reward, done, info):\n        if info.get(\"action_type\") != \"fix\":\n            return 0.0\n        score = 0.0\n        if not env._tests_run:\n            score -= 0.25\n        if env._step_count < 2:\n            score -= 0.1\n        if env._tests_run and env._linter_run:\n            score += 0.02\n        return score\n\n\n# --------------------------------------------------------------------------------\n# 6. STEP PENALTY\n# --------------------------------------------------------------------------------\nclass StepPenaltyRubric(Rubric):\n    def __init__(self, penalty: float = -0.01):\n        self.penalty = penalty\n\n    def __call__(self, env, action, obs, reward, done, info):\n        return self.penalty\n\nimport json\ndummy_training_data = [\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef get_user_name(user):\\n    return user.name.upper()\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"inspect\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef get_user_name(user):\\n    return user.name.upper()\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\ndef get_user_name(user):\\n    return user.name.upper()\\n# No None-guard before accessing user.name\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_tests\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef get_user_name(user):\\n    return user.name.upper()\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nFAILED test_get_user_name_none ... AttributeError: 'NoneType' object has no attribute 'name'\\nPASSED test_get_user_name_valid\\nResults: 1 passed, 1 failed (50.0%)\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_linter\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef get_user_name(user):\\n    return user.name.upper()\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nW0104 null_check.py:2:0: Statement seems to have no effect (pointless-statement)\\nE1101 null_check.py:2:11: Instance of 'NoneType' has no 'name' member (no-member)\\nYour code has been rated at 3.50/10\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"query_docs\\\", \\\"content\\\": \\\"None guard before attribute access Python\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef get_user_name(user):\\n    return user.name.upper()\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nPEP 8 / Python docs: Always guard attribute access on potentially-None objects.\\nPattern: `if obj is None: return default` before accessing `obj.attr`.\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"fix\\\", \\\"content\\\": \\\"def fix(user):\\\\n    if user is None:\\\\n        return \\\\\\\"\\\\\\\"\\\\n    return user.name.upper()\\\\n\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef get_user_name(user):\\n    return user.name.upper()\\n\\n\\nAuthor says:\\nI'm not convinced. The test failure could be a bad test, not a real bug. Why does this matter in production?\\n\\nLast tool output:\\nFix proposed. Tests: 2/2 passed. Lint: 10/10.\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"comment\\\", \\\"content\\\": \\\"The fix is necessary because in production `user` can legally be None when no record is found in the database. Without the guard, the code raises AttributeError at runtime, which crashes the caller. The None-guard follows the documented Python best practice (PEP 8). Tests now pass 2/2 and pylint rates the fixed code 10/10, therefore this is a genuine defect, not a flawed test.\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef get_user_name(user):\\n    return user.name.upper()\\n\\n\\nAuthor says:\\nFair enough. The evidence is solid\\u2014tests now pass, lint is clean, and the None-guard follows documented Python best practice. I'll merge this.\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"done\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef add(a, b):\\n    resutl = a + b\\n    return result\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"inspect\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef add(a, b):\\n    resutl = a + b\\n    return result\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\ndef add(a, b):\\n    resutl = a + b   # typo: should be \\\"result\\\"\\n    return result    # NameError: result is not defined\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_tests\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef add(a, b):\\n    resutl = a + b\\n    return result\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nFAILED test_add ... NameError: name 'result' is not defined\\nResults: 0 passed, 3 failed (0.0%)\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_linter\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef add(a, b):\\n    resutl = a + b\\n    return result\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nE0602 add.py:3:11: Undefined variable 'result' (undefined-variable)\\nYour code has been rated at 0.00/10\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"query_docs\\\", \\\"content\\\": \\\"Python NameError undefined variable\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef add(a, b):\\n    resutl = a + b\\n    return result\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nPython scoping rules: variables must be assigned before use in the same scope.\\nA NameError is raised when a name is not found in the local or global namespace.\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"fix\\\", \\\"content\\\": \\\"def fix(a, b):\\\\n    result = a + b\\\\n    return result\\\\n\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef add(a, b):\\n    resutl = a + b\\n    return result\\n\\n\\nAuthor says:\\nThis looks trivial. Are you sure this is the only issue? Maybe the logic is wrong too?\\n\\nLast tool output:\\nFix proposed. Tests: 3/3 passed. Lint: 10/10.\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"comment\\\", \\\"content\\\": \\\"The fix corrects only the variable name typo: `resutl` \\\\u2192 `result`. The arithmetic logic `a + b` is correct, therefore this single-character transposition is the sole defect. After fixing, all 3 tests pass (100%) and pylint gives a perfect 10/10. Python's NameError documentation confirms that undefined names cause immediate runtime failures, exactly what we observed.\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef add(a, b):\\n    resutl = a + b\\n    return result\\n\\n\\nAuthor says:\\nYou've shown the variable name is the sole cause of failure. Lint and tests both confirm it. Accepted.\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"done\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef first_char(s):\\n    return s[1]\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"inspect\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef first_char(s):\\n    return s[1]\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\ndef first_char(s):\\n    return s[1]   # Bug: index 1 is the SECOND character; should be s[0]\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_tests\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef first_char(s):\\n    return s[1]\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nFAILED test_first_char_hello ... AssertionError: expected 'H', got 'e'\\nFAILED test_first_char_abc   ... AssertionError: expected 'a', got 'b'\\nPASSED test_first_char_single (single-char strings masked the bug partially)\\nResults: 1 passed, 2 failed (33.3%)\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_linter\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef first_char(s):\\n    return s[1]\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nC0301 string_index.py:2:11: Magic value used in expression (magic-value-used)\\nYour code has been rated at 8.50/10\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"query_docs\\\", \\\"content\\\": \\\"Python zero-based string indexing\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef first_char(s):\\n    return s[1]\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nPython sequences are zero-indexed: s[0] is the first element, s[1] is the second.\\ndocs.python.org/3/library/stdtypes.html#sequence-types\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"fix\\\", \\\"content\\\": \\\"def fix(s):\\\\n    return s[0]\\\\n\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef first_char(s):\\n    return s[1]\\n\\n\\nAuthor says:\\nIt passes one test already with s[1]. Maybe the spec is for the second character?\\n\\nLast tool output:\\nFix proposed. Tests: 3/3 passed. Lint: 10/10.\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"comment\\\", \\\"content\\\": \\\"The function is named `first_char`, which unambiguously specifies the first character. Python strings are zero-indexed (docs.python.org confirms s[0] is the first element), therefore s[1] is incorrect. The single-char test passed with s[1] only because a one-character string has no index 1, so Python returned the same character. Changing to s[0] passes all 3 tests (100%) with a clean lint score.\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef first_char(s):\\n    return s[1]\\n\\n\\nAuthor says:\\nThe test names clearly say 'first_char' and the docs back zero-indexing. Merging.\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"done\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef repeat(s, n=0):\\n    return s * n\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"inspect\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef repeat(s, n=0):\\n    return s * n\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\ndef repeat(s, n=0):\\n    return s * n\\n# Default n=0 means repeat(\\\"hi\\\") returns \\\"\\\" \\u2013 almost certainly wrong; should be n=1\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_tests\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef repeat(s, n=0):\\n    return s * n\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nFAILED test_repeat_default ... AssertionError: expected 'hi', got ''\\nPASSED test_repeat_3       ... 'hihihi'\\nResults: 1 passed, 1 failed (50.0%)\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_linter\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef repeat(s, n=0):\\n    return s * n\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nW0102 repeat.py:1:14: Dangerous default value 0 (not literally dangerous but unexpected)\\nYour code has been rated at 7.50/10\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"query_docs\\\", \\\"content\\\": \\\"Python default parameter value convention\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef repeat(s, n=0):\\n    return s * n\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nPython docs: default argument values are evaluated once. A default of n=1 for a repeat\\nfunction matches the mathematical identity: repeating once yields the original string.\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"fix\\\", \\\"content\\\": \\\"def fix(s, n=1):\\\\n    return s * n\\\\n\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef repeat(s, n=0):\\n    return s * n\\n\\n\\nAuthor says:\\nWho says n=1 is correct? Maybe the caller always supplies n anyway.\\n\\nLast tool output:\\nFix proposed. Tests: 2/2 passed. Lint: 10/10.\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"comment\\\", \\\"content\\\": \\\"The test suite explicitly asserts that `repeat('hi')` (no n) returns `'hi'`, therefore the default must be 1, not 0. A default of 0 is mathematically wrong because multiplying any string by 0 erases it, which is the multiplicative-zero identity\\\\u2014not the multiplicative-one identity expected for a no-op repeat. Even if callers always supplied n, n=0 as a default is a silent footgun for future callers. After the fix, 2/2 tests pass and lint is clean.\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef repeat(s, n=0):\\n    return s * n\\n\\n\\nAuthor says:\\nTest evidence and the mathematical identity argument are convincing. Accepted.\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"done\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef square(x):\\n    x * x\\n    return\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"inspect\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef square(x):\\n    x * x\\n    return\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\ndef square(x):\\n    x * x    # result computed but discarded\\n    return   # bare return yields None \\u2013 should be: return x * x\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_tests\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef square(x):\\n    x * x\\n    return\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nFAILED test_square_2 ... AssertionError: expected 4, got None\\nFAILED test_square_0 ... AssertionError: expected 0, got None\\nResults: 0 passed, 3 failed (0.0%)\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_linter\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef square(x):\\n    x * x\\n    return\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nW0104 square.py:2:4: Statement seems to have no effect (pointless-statement)\\nW0107 square.py:3:4: Unnecessary pass or return (unnecessary-return)\\nYour code has been rated at 2.00/10\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"query_docs\\\", \\\"content\\\": \\\"Python bare return None vs return value\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef square(x):\\n    x * x\\n    return\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nPython: a bare `return` statement returns None. To return a value, use `return <expression>`.\\nThe computed expression `x * x` on its own line has no effect (pylint W0104).\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"fix\\\", \\\"content\\\": \\\"def fix(x):\\\\n    return x * x\\\\n\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef square(x):\\n    x * x\\n    return\\n\\n\\nAuthor says:\\nCould the callers just handle None? Why change the function?\\n\\nLast tool output:\\nFix proposed. Tests: 3/3 passed. Lint: 10/10.\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"comment\\\", \\\"content\\\": \\\"Patching callers to handle None would spread the defect throughout the codebase, therefore it is far safer to fix the function itself. The function is named `square` and is documented to return a number; returning None violates that contract. Pylint W0104 already flags the dead computation on line 2, confirming the bug. After the fix, all 3 tests pass (100%) and pylint rates the code 10/10.\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef square(x):\\n    x * x\\n    return\\n\\n\\nAuthor says:\\nAll tests pass, lint is clean, and the logic is clearly correct. I'll accept.\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"done\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef sum_list(lst):\\n    total = 0\\n    for i in range(len(lst) - 1):\\n        total += lst[i]\\n    return total\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"inspect\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef sum_list(lst):\\n    total = 0\\n    for i in range(len(lst) - 1):\\n        total += lst[i]\\n    return total\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\ndef sum_list(lst):\\n    total = 0\\n    for i in range(len(lst) - 1):  # Bug: skips last element\\n        total += lst[i]\\n    return total\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_tests\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef sum_list(lst):\\n    total = 0\\n    for i in range(len(lst) - 1):\\n        total += lst[i]\\n    return total\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nFAILED test_sum_list_basic ... AssertionError: expected 15, got 10\\nFAILED test_sum_list_single... AssertionError: expected 7, got 0\\nPASSED test_sum_list_empty\\nResults: 1 passed, 2 failed (33.3%)\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_linter\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef sum_list(lst):\\n    total = 0\\n    for i in range(len(lst) - 1):\\n        total += lst[i]\\n    return total\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nC0200 sum_list.py:3:4: Consider using enumerate instead of range+len (consider-using-enumerate)\\nYour code has been rated at 7.00/10\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"query_docs\\\", \\\"content\\\": \\\"Python range off-by-one list iteration\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef sum_list(lst):\\n    total = 0\\n    for i in range(len(lst) - 1):\\n        total += lst[i]\\n    return total\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nPython docs: range(n) produces 0..n-1. To iterate all indices of a list use range(len(lst)).\\nUsing `range(len(lst)-1)` skips the last element (off-by-one error).\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"fix\\\", \\\"content\\\": \\\"def fix(lst):\\\\n    total = 0\\\\n    for item in lst:\\\\n        total += item\\\\n    return total\\\\n\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef sum_list(lst):\\n    total = 0\\n    for i in range(len(lst) - 1):\\n        total += lst[i]\\n    return total\\n\\n\\nAuthor says:\\nThe empty list passes. That's good enough for our use case, isn't it?\\n\\nLast tool output:\\nFix proposed. Tests: 3/3 passed. Lint: 10/10.\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"comment\\\", \\\"content\\\": \\\"The empty-list test passes coincidentally because the loop body never executes. However, for [1,2,3,4,5] the function returns 10 instead of 15, silently dropping the last element. This is a classic off-by-one: `range(len(lst)-1)` stops one index short. The Python docs confirm that `range(len(lst))` is required to cover all indices. The fix iterates directly over items, which is also cleaner (lint approves). All 3 tests now pass (100%).\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef sum_list(lst):\\n    total = 0\\n    for i in range(len(lst) - 1):\\n        total += lst[i]\\n    return total\\n\\n\\nAuthor says:\\nYou've demonstrated the last-element skip with concrete numbers and docs. Merging.\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"done\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef find_max(lst):\\n    max_val = lst[0]\\n    for item in lst[2:]:\\n        if item > max_val:\\n            max_val = item\\n    return max_val\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"inspect\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef find_max(lst):\\n    max_val = lst[0]\\n    for item in lst[2:]:\\n        if item > max_val:\\n            max_val = item\\n    return max_val\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\ndef find_max(lst):\\n    max_val = lst[0]\\n    for item in lst[1:]:   # Starts from index 1, which is correct here\\n        if item > max_val:\\n            max_val = item\\n    return max_val\\n# Wait - actually this looks correct. Let me re-examine the buggy version...\\n# BUG version: lst[2:] skips index 0 AND 1, so max_val init could be wrong if lst[1] is larger\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_tests\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef find_max(lst):\\n    max_val = lst[0]\\n    for item in lst[2:]:\\n        if item > max_val:\\n            max_val = item\\n    return max_val\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nFAILED test_find_max_second_largest_at_1 ... AssertionError: expected 9, got 5\\n# Input was [5, 9, 3, 1] -- lst[1]=9 is max but skipped\\nPASSED test_find_max_max_at_0\\nPASSED test_find_max_max_at_end\\nResults: 2 passed, 1 failed (66.7%)\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_linter\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef find_max(lst):\\n    max_val = lst[0]\\n    for item in lst[2:]:\\n        if item > max_val:\\n            max_val = item\\n    return max_val\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nC0200 find_max.py:3:4: Use enumerate instead of manual slice (consider-using-enumerate)\\nYour code has been rated at 8.00/10\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"query_docs\\\", \\\"content\\\": \\\"Python list slice indices off-by-one\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef find_max(lst):\\n    max_val = lst[0]\\n    for item in lst[2:]:\\n        if item > max_val:\\n            max_val = item\\n    return max_val\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nPython slice lst[2:] omits indices 0 and 1. To scan all elements after the seed,\\nuse lst[1:] (skipping only the seed at index 0) or iterate the full list and skip max_val re-init.\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"fix\\\", \\\"content\\\": \\\"def fix(lst):\\\\n    max_val = lst[0]\\\\n    for item in lst[1:]:\\\\n        if item > max_val:\\\\n            max_val = item\\\\n    return max_val\\\\n\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef find_max(lst):\\n    max_val = lst[0]\\n    for item in lst[2:]:\\n        if item > max_val:\\n            max_val = item\\n    return max_val\\n\\n\\nAuthor says:\\nTwo tests pass. Is that one failure really due to the slice and not a bad test case?\\n\\nLast tool output:\\nFix proposed. Tests: 3/3 passed. Lint: 10/10.\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"comment\\\", \\\"content\\\": \\\"Tracing manually: lst = [5, 9, 3, 1], max_val = lst[0] = 5, then lst[2:] = [3, 1]. Neither 3 nor 1 exceeds 5, so the function returns 5\\\\u2014wrong. The maximum is 9 at index 1, which is skipped because the slice starts at index 2. Therefore the test is correct and the bug is real. The fix uses lst[1:], which includes index 1 (the seed's immediate neighbor), resolving the skip. All 3 tests now pass (100%).\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef find_max(lst):\\n    max_val = lst[0]\\n    for item in lst[2:]:\\n        if item > max_val:\\n            max_val = item\\n    return max_val\\n\\n\\nAuthor says:\\nThe concrete trace and documentation prove it. I'll merge.\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"done\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef celsius_to_fahrenheit(c):\\n    return c * 9 / 5 - 32\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"inspect\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef celsius_to_fahrenheit(c):\\n    return c * 9 / 5 - 32\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\ndef celsius_to_fahrenheit(c):\\n    return c * 9 / 5 - 32   # Bug: should be + 32\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_tests\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef celsius_to_fahrenheit(c):\\n    return c * 9 / 5 - 32\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nFAILED test_boiling  ... AssertionError: expected 212, got 148.0\\nFAILED test_freezing ... AssertionError: expected 32, got -32.0\\nFAILED test_body     ... AssertionError: expected 98.6, got 34.6\\nResults: 0 passed, 3 failed (0.0%)\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_linter\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef celsius_to_fahrenheit(c):\\n    return c * 9 / 5 - 32\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nYour code has been rated at 10.00/10\\n# Lint passes \\u2013 this is a pure logic bug\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"query_docs\\\", \\\"content\\\": \\\"Celsius to Fahrenheit conversion formula\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef celsius_to_fahrenheit(c):\\n    return c * 9 / 5 - 32\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nFormula: F = C \\u00d7 9/5 + 32  (Wikipedia: Fahrenheit)\\nAt C=0 (freezing): F = 0 * 9/5 + 32 = 32. The code produces -32, confirming sign error.\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"fix\\\", \\\"content\\\": \\\"def fix(c):\\\\n    return c * 9 / 5 + 32\\\\n\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef celsius_to_fahrenheit(c):\\n    return c * 9 / 5 - 32\\n\\n\\nAuthor says:\\nLint is 10/10, so the code looks fine to me. Why change something pylint approves?\\n\\nLast tool output:\\nFix proposed. Tests: 3/3 passed. Lint: 10/10.\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"comment\\\", \\\"content\\\": \\\"Pylint checks style and syntax, not mathematical correctness, therefore a 10/10 lint score does not validate the formula. The documented formula is F = C \\\\u00d7 9/5 + 32 (Wikipedia, NIST). Substituting the freezing point: 0 \\\\u00d7 9/5 + 32 = 32 \\\\u2713. The buggy code produces 0 \\\\u2212 32 = \\\\u221232 \\\\u2717. All three tests (boiling, freezing, body temperature) failed with the minus sign and all three pass after correcting it to +32.\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef celsius_to_fahrenheit(c):\\n    return c * 9 / 5 - 32\\n\\n\\nAuthor says:\\nThe formula reference and three failing tests are conclusive. Will merge.\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"done\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef power(base, exponent):\\n    return exponent ** base\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"inspect\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef power(base, exponent):\\n    return exponent ** base\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\ndef power(base, exponent):\\n    return exponent ** base   # Bug: operands swapped; should be base ** exponent\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_tests\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef power(base, exponent):\\n    return exponent ** base\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nFAILED test_power_2_3 ... AssertionError: expected 8, got 9\\nFAILED test_power_10_0... AssertionError: expected 1, got 0\\nPASSED test_power_2_1  (2**1 == 1**2 == 2, masks bug)\\nResults: 1 passed, 2 failed (33.3%)\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_linter\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef power(base, exponent):\\n    return exponent ** base\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nYour code has been rated at 10.00/10\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"query_docs\\\", \\\"content\\\": \\\"Python exponentiation operator operand order\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef power(base, exponent):\\n    return exponent ** base\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nPython ** operator: x ** y means x raised to the power y.\\npower(2, 3) should return 2**3=8; swapping gives 3**2=9.\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"fix\\\", \\\"content\\\": \\\"def fix(base, exponent):\\\\n    return base ** exponent\\\\n\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef power(base, exponent):\\n    return exponent ** base\\n\\n\\nAuthor says:\\nThe symmetric test (2^1) passes. Doesn't that mean the implementation is fine?\\n\\nLast tool output:\\nFix proposed. Tests: 3/3 passed. Lint: 10/10.\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"comment\\\", \\\"content\\\": \\\"The symmetric test `power(2,1)` passes because 2**1 = 1**2 = 2, which masks the bug. However `power(2,3)` returns 3**2=9 instead of 2**3=8, and `power(10,0)` returns 0**10=0 instead of 10**0=1. Both failures are explained by the swapped operands. Python's ** operator is documented as `base ** exponent`, therefore the correct expression is `base ** exponent`. After the fix, all 3 tests pass (100%).\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef power(base, exponent):\\n    return exponent ** base\\n\\n\\nAuthor says:\\nSwapped operands proven by test outputs and operator docs. Merging.\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"done\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef cumulative_sum(lst):\\n    result = []\\n    for item in lst:\\n        running += item\\n        result.append(running)\\n    return result\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"inspect\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef cumulative_sum(lst):\\n    result = []\\n    for item in lst:\\n        running += item\\n        result.append(running)\\n    return result\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\ndef cumulative_sum(lst):\\n    result = []\\n    for item in lst:\\n        running += item    # Bug: `running` never initialised; UnboundLocalError\\n        result.append(running)\\n    return result\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_tests\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef cumulative_sum(lst):\\n    result = []\\n    for item in lst:\\n        running += item\\n        result.append(running)\\n    return result\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nFAILED test_cumsum_basic ... UnboundLocalError: local variable 'running' referenced before assignment\\nFAILED test_cumsum_empty (no crash but wrong \\u2013 returns [] which may be acceptable)\\nResults: 0 passed, 2 failed (0.0%)\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_linter\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef cumulative_sum(lst):\\n    result = []\\n    for item in lst:\\n        running += item\\n        result.append(running)\\n    return result\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nE0601 cumsum.py:4:8: Using variable 'running' before assignment (used-before-assignment)\\nYour code has been rated at 1.50/10\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"query_docs\\\", \\\"content\\\": \\\"Python UnboundLocalError variable initialisation before loop\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef cumulative_sum(lst):\\n    result = []\\n    for item in lst:\\n        running += item\\n        result.append(running)\\n    return result\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nPython: augmented assignment (+=) requires the variable to already exist in the current scope.\\nFix: initialise `running = 0` before the loop.\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"fix\\\", \\\"content\\\": \\\"def fix(lst):\\\\n    result = []\\\\n    running = 0\\\\n    for item in lst:\\\\n        running += item\\\\n        result.append(running)\\\\n    return result\\\\n\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef cumulative_sum(lst):\\n    result = []\\n    for item in lst:\\n        running += item\\n        result.append(running)\\n    return result\\n\\n\\nAuthor says:\\nCouldn't we just catch the UnboundLocalError and default to 0 inside the loop?\\n\\nLast tool output:\\nFix proposed. Tests: 2/2 passed. Lint: 10/10.\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"comment\\\", \\\"content\\\": \\\"Using try/except to catch UnboundLocalError would be an anti-pattern because the error is a programming mistake, not an expected runtime condition. Python's own documentation states that augmented assignment requires prior assignment, therefore the correct fix is to initialise `running = 0` before the loop\\\\u2014which is O(1) overhead and semantically clear. The fix passes all 2 tests (100%) and raises the lint score from 1.5/10 to 10/10.\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef cumulative_sum(lst):\\n    result = []\\n    for item in lst:\\n        running += item\\n        result.append(running)\\n    return result\\n\\n\\nAuthor says:\\nProper initialisation is cleaner than exception handling. Tests and lint confirm. Accepted.\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"done\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef average(lst):\\n    return sum(lst) / len(lst)\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"inspect\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef average(lst):\\n    return sum(lst) / len(lst)\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\ndef average(lst):\\n    return sum(lst) / len(lst)   # ZeroDivisionError when lst is empty\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_tests\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef average(lst):\\n    return sum(lst) / len(lst)\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nFAILED test_average_empty ... ZeroDivisionError: division by zero\\nPASSED test_average_basic\\nPASSED test_average_single\\nResults: 2 passed, 1 failed (66.7%)\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_linter\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef average(lst):\\n    return sum(lst) / len(lst)\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nYour code has been rated at 10.00/10\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"query_docs\\\", \\\"content\\\": \\\"Python division by zero empty list average guard\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef average(lst):\\n    return sum(lst) / len(lst)\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nPython: dividing by zero raises ZeroDivisionError. Guard with `if not lst: return 0.0` (or raise ValueError).\\nstatistics.mean() raises StatisticsError on empty input; same principle applies.\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"fix\\\", \\\"content\\\": \\\"def fix(lst):\\\\n    if not lst:\\\\n        return 0.0\\\\n    return sum(lst) / len(lst)\\\\n\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef average(lst):\\n    return sum(lst) / len(lst)\\n\\n\\nAuthor says:\\nCallers should never pass empty lists. This is their responsibility.\\n\\nLast tool output:\\nFix proposed. Tests: 3/3 passed. Lint: 10/10.\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"comment\\\", \\\"content\\\": \\\"Relying on callers to never pass an empty list is fragile because any future refactor or new call site can violate that assumption silently. Python's `statistics.mean()` raises a StatisticsError on empty input rather than crashing with an unguarded division\\\\u2014confirming that the standard library itself treats this as a function responsibility. The guard costs one condition and prevents an uncaught ZeroDivisionError that would propagate up the call stack. All 3 tests now pass (100%).\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef average(lst):\\n    return sum(lst) / len(lst)\\n\\n\\nAuthor says:\\nDefensive guard makes the API robust. Tests and docs back it. Merging.\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"done\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef safe_divide(a, b):\\n    return a / b\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"inspect\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef safe_divide(a, b):\\n    return a / b\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\ndef safe_divide(a, b):\\n    return a / b   # No guard for b == 0 despite the function name suggesting safety\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_tests\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef safe_divide(a, b):\\n    return a / b\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nFAILED test_divide_by_zero  ... ZeroDivisionError: division by zero\\nPASSED test_divide_normal\\nPASSED test_divide_negative\\nResults: 2 passed, 1 failed (66.7%)\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_linter\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef safe_divide(a, b):\\n    return a / b\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nYour code has been rated at 10.00/10\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"query_docs\\\", \\\"content\\\": \\\"Python safe_divide zero guard ValueError convention\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef safe_divide(a, b):\\n    return a / b\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nPython ZeroDivisionError docs: raised when second arg of division is zero.\\nConvention for safe_divide: return float('inf') or raise ValueError with descriptive message.\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"fix\\\", \\\"content\\\": \\\"def fix(a, b):\\\\n    if b == 0:\\\\n        raise ValueError(f\\\\\\\"Cannot divide {a} by zero\\\\\\\")\\\\n    return a / b\\\\n\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef safe_divide(a, b):\\n    return a / b\\n\\n\\nAuthor says:\\nThe function name has 'safe' but maybe it just means safe in context, not zero-guarded.\\n\\nLast tool output:\\nFix proposed. Tests: 3/3 passed. Lint: 10/10.\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"comment\\\", \\\"content\\\": \\\"The function is explicitly named `safe_divide`. In Python conventions, 'safe' in a function name signals that the function handles edge cases gracefully rather than propagating raw exceptions to the caller. Raising a descriptive ValueError with a clear message ('Cannot divide X by zero') is the idiomatic pattern, as opposed to letting ZeroDivisionError leak out. The test suite confirms that callers expect a handled outcome. All 3 tests pass after the fix (100%).\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef safe_divide(a, b):\\n    return a / b\\n\\n\\nAuthor says:\\nThe ValueError contract is explicit and well-documented. Will merge.\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"done\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef is_equal_float(a, b):\\n    return a == b\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"inspect\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef is_equal_float(a, b):\\n    return a == b\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\ndef is_equal_float(a, b):\\n    return a == b   # Exact equality for floats fails due to IEEE 754 representation errors\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_tests\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef is_equal_float(a, b):\\n    return a == b\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nFAILED test_float_equal_computed ... AssertionError: expected True, got False\\n# 0.1 + 0.2 == 0.3 is False in CPython\\nPASSED test_float_equal_same_literal\\nResults: 1 passed, 1 failed (50.0%)\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_linter\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef is_equal_float(a, b):\\n    return a == b\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nYour code has been rated at 10.00/10\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"query_docs\\\", \\\"content\\\": \\\"Python float equality IEEE 754 math.isclose\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef is_equal_float(a, b):\\n    return a == b\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nPython docs (math.isclose): \\\"Determine whether two floating-point numbers are close in value.\\\"\\nmath.isclose(a, b, rel_tol=1e-9) is the recommended replacement for == on floats.\\nIEEE 754: 0.1 + 0.2 = 0.30000000000000004, not 0.3.\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"fix\\\", \\\"content\\\": \\\"import math\\\\n\\\\ndef fix(a, b, rel_tol=1e-9):\\\\n    return math.isclose(a, b, rel_tol=rel_tol)\\\\n\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef is_equal_float(a, b):\\n    return a == b\\n\\n\\nAuthor says:\\nWe control all inputs; they're all simple decimals. This edge case won't happen in practice.\\n\\nLast tool output:\\nFix proposed. Tests: 2/2 passed. Lint: 10/10.\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"comment\\\", \\\"content\\\": \\\"Even 'simple' decimals like 0.1 and 0.2 cannot be represented exactly in IEEE 754 double precision, therefore 0.1 + 0.2 evaluates to 0.30000000000000004 in CPython\\\\u2014a fact reproducible in any Python REPL. This is not an exotic edge case; it is the default floating-point behaviour. The Python standard library provides `math.isclose()` precisely to handle this, with documentation explicitly recommending it over `==` for floats. All tests pass after the fix (100%).\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef is_equal_float(a, b):\\n    return a == b\\n\\n\\nAuthor says:\\nmath.isclose is the documented Python recommendation. Evidence is solid. Merging.\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"done\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef distance(a, b):\\n    return a - b\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"inspect\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef distance(a, b):\\n    return a - b\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\ndef distance(a, b):\\n    return a - b   # Bug: distance should be non-negative; missing abs()\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_tests\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef distance(a, b):\\n    return a - b\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nFAILED test_distance_b_gt_a ... AssertionError: expected 3, got -3\\nPASSED test_distance_a_gt_b\\nPASSED test_distance_equal\\nResults: 2 passed, 1 failed (33.3%)\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_linter\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef distance(a, b):\\n    return a - b\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nYour code has been rated at 10.00/10\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"query_docs\\\", \\\"content\\\": \\\"Python abs() distance absolute value\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef distance(a, b):\\n    return a - b\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nMathematical definition: distance is always non-negative. |a - b| = |b - a|.\\nPython built-in abs(x) returns the absolute value of a number.\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"fix\\\", \\\"content\\\": \\\"def fix(a, b):\\\\n    return abs(a - b)\\\\n\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef distance(a, b):\\n    return a - b\\n\\n\\nAuthor says:\\nIf a > b, it works. Our callers always pass a > b.\\n\\nLast tool output:\\nFix proposed. Tests: 3/3 passed. Lint: 10/10.\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"comment\\\", \\\"content\\\": \\\"Assuming callers always pass a > b is an undocumented precondition that the function signature does not enforce. Any future caller (or test) that passes b > a will receive a negative distance, which violates the mathematical definition. The mathematical distance |a - b| = |b - a| is symmetric and always non-negative. Python's built-in `abs()` is the idiomatic way to express this. Using abs() is a zero-cost, one-character change that makes the contract correct for all inputs.\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef distance(a, b):\\n    return a - b\\n\\n\\nAuthor says:\\nabs() is correct for the mathematical definition. All tests pass. Merging.\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"done\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef average_rounded(lst):\\n    rounded = [round(x) for x in lst]\\n    return sum(rounded) / len(rounded)\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"inspect\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef average_rounded(lst):\\n    rounded = [round(x) for x in lst]\\n    return sum(rounded) / len(rounded)\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\ndef average_rounded(lst):\\n    rounded = [round(x) for x in lst]   # Bug: rounding before averaging introduces bias\\n    return sum(rounded) / len(rounded)\\n# Correct approach: compute average first, then round the result\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_tests\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef average_rounded(lst):\\n    rounded = [round(x) for x in lst]\\n    return sum(rounded) / len(rounded)\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nFAILED test_average_rounded_bias ... AssertionError: expected 1.5, got 2.0\\n# [1.4, 1.6] -> rounded [1, 2] -> avg 1.5 vs [1,2] avg 1.5... let me recalc\\n# [1.1, 1.9] -> rounded [1, 2] -> avg 1.5 \\u2713 but [0.5, 2.5] -> [0,2] (banker's) -> 1.0 vs 1.5\\nPASSED test_average_rounded_integer\\nResults: 1 passed, 1 failed (50.0%)\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_linter\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef average_rounded(lst):\\n    rounded = [round(x) for x in lst]\\n    return sum(rounded) / len(rounded)\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nYour code has been rated at 9.50/10\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"query_docs\\\", \\\"content\\\": \\\"Python rounding bias aggregate first then round statistics\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef average_rounded(lst):\\n    rounded = [round(x) for x in lst]\\n    return sum(rounded) / len(rounded)\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nStatistics: rounding individual values before aggregation introduces rounding bias.\\nBest practice: aggregate first, then round. round(sum(lst)/len(lst), ndigits).\\nPython docs: round() uses banker's rounding (round half to even).\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"fix\\\", \\\"content\\\": \\\"def fix(lst):\\\\n    if not lst:\\\\n        return 0.0\\\\n    return round(sum(lst) / len(lst), 2)\\\\n\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef average_rounded(lst):\\n    rounded = [round(x) for x in lst]\\n    return sum(rounded) / len(rounded)\\n\\n\\nAuthor says:\\nWe always use integer inputs so the pre-rounding doesn't matter.\\n\\nLast tool output:\\nFix proposed. Tests: 2/2 passed. Lint: 10/10.\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"comment\\\", \\\"content\\\": \\\"If inputs are always integers, round(integer) = integer, so the current code would not cause problems\\\\u2014but that assumption is not enforced by the type signature. The function is named `average_rounded`, implying it should handle any numeric input. With [0.5, 2.5], Python's banker's rounding produces [0, 2] \\\\u2192 average 1.0 instead of the correct 1.5. The statistical principle is to aggregate first, then round, therefore `round(sum(lst)/len(lst))` is correct. The fix also guards against empty lists (division by zero). All tests pass.\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\ndef average_rounded(lst):\\n    rounded = [round(x) for x in lst]\\n    return sum(rounded) / len(rounded)\\n\\n\\nAuthor says:\\nThe statistical bias argument and docs reference are solid. Will merge.\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"done\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\ncounter = 0\\n\\ndef increment():\\n    global counter\\n    counter += 1\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"inspect\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\ncounter = 0\\n\\ndef increment():\\n    global counter\\n    counter += 1\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nimport threading\\ncounter = 0\\n\\ndef increment():\\n    global counter\\n    counter += 1   # Bug: counter += 1 is not atomic; race condition without a lock\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_tests\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\ncounter = 0\\n\\ndef increment():\\n    global counter\\n    counter += 1\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nFAILED test_concurrent_increment ... AssertionError: expected 1000, got 987\\n# Non-deterministic; sometimes passes\\nResults: 0 passed, 1 failed (with 10 runs: 7 failed, 3 passed)\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_linter\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\ncounter = 0\\n\\ndef increment():\\n    global counter\\n    counter += 1\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nW0603 thread_counter.py:6:4: Using the global statement (global-statement)\\nYour code has been rated at 6.00/10\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"query_docs\\\", \\\"content\\\": \\\"Python GIL thread safety counter increment lock\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\ncounter = 0\\n\\ndef increment():\\n    global counter\\n    counter += 1\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nPython threading docs: \\\"The GIL does not protect all operations. counter += 1 is multiple\\nbytecode instructions (LOAD, INCR, STORE) and a context switch can occur between them.\\\"\\nthreading.Lock() should be used to serialise access to shared mutable state.\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"fix\\\", \\\"content\\\": \\\"import threading\\\\n\\\\n_lock = threading.Lock()\\\\ncounter = 0\\\\n\\\\ndef fix():\\\\n    global counter\\\\n    with _lock:\\\\n        counter += 1\\\\n\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\ncounter = 0\\n\\ndef increment():\\n    global counter\\n    counter += 1\\n\\n\\nAuthor says:\\nThe GIL protects Python code. We don't need locks.\\n\\nLast tool output:\\nFix proposed. Tests: consistently 10/10 passed. Lint: 8/10.\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"comment\\\", \\\"content\\\": \\\"The GIL is a common misconception: it does NOT make compound operations atomic. `counter += 1` compiles to LOAD_GLOBAL, BINARY_ADD, STORE_GLOBAL\\\\u2014three separate bytecode instructions. The GIL can be released between any two of them, allowing a second thread to read the stale value. The Python threading documentation explicitly warns about this pattern. Our test demonstrates the race: with 1000 threads, the counter reliably ends below 1000 without a lock. With `threading.Lock()`, the test passes every run (10/10). Therefore a lock is necessary.\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\ncounter = 0\\n\\ndef increment():\\n    global counter\\n    counter += 1\\n\\n\\nAuthor says:\\nThe GIL clarification and test evidence are convincing. Merging.\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"done\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_lock = threading.Lock()\\n\\ndef transfer(account, amount):\\n    with _lock:\\n        with _lock:   # Bug: re-acquiring non-reentrant Lock causes deadlock\\n            account[\\\"balance\\\"] -= amount\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"inspect\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_lock = threading.Lock()\\n\\ndef transfer(account, amount):\\n    with _lock:\\n        with _lock:   # Bug: re-acquiring non-reentrant Lock causes deadlock\\n            account[\\\"balance\\\"] -= amount\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nimport threading\\n_lock = threading.Lock()\\n\\ndef transfer(account, amount):\\n    with _lock:\\n        with _lock:   # threading.Lock() is NOT reentrant; second acquire deadlocks\\n            account[\\\"balance\\\"] -= amount\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_tests\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_lock = threading.Lock()\\n\\ndef transfer(account, amount):\\n    with _lock:\\n        with _lock:   # Bug: re-acquiring non-reentrant Lock causes deadlock\\n            account[\\\"balance\\\"] -= amount\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nFAILED test_transfer_basic ... test timed out after 5s (deadlock)\\nResults: 0 passed, 1 failed (0.0%)\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_linter\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_lock = threading.Lock()\\n\\ndef transfer(account, amount):\\n    with _lock:\\n        with _lock:   # Bug: re-acquiring non-reentrant Lock causes deadlock\\n            account[\\\"balance\\\"] -= amount\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nW0612 double_lock.py:6:8: Unused variable (false positive from nested with)\\nYour code has been rated at 7.00/10\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"query_docs\\\", \\\"content\\\": \\\"Python threading.Lock reentrant deadlock double acquire\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_lock = threading.Lock()\\n\\ndef transfer(account, amount):\\n    with _lock:\\n        with _lock:   # Bug: re-acquiring non-reentrant Lock causes deadlock\\n            account[\\\"balance\\\"] -= amount\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nPython docs: threading.Lock() is NOT reentrant. Calling acquire() on an already-held Lock\\nfrom the same thread blocks forever. Use threading.RLock() if reentrant locking is required,\\nor remove the duplicate lock acquisition.\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"fix\\\", \\\"content\\\": \\\"import threading\\\\n\\\\n_lock = threading.Lock()\\\\n\\\\ndef fix(account, amount):\\\\n    with _lock:\\\\n        account[\\\\\\\"balance\\\\\\\"] -= amount\\\\n\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_lock = threading.Lock()\\n\\ndef transfer(account, amount):\\n    with _lock:\\n        with _lock:   # Bug: re-acquiring non-reentrant Lock causes deadlock\\n            account[\\\"balance\\\"] -= amount\\n\\n\\nAuthor says:\\nNested locks add safety layers. Why would removing one be safer?\\n\\nLast tool output:\\nFix proposed. Tests: 1/1 passed. Lint: 9/10.\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"comment\\\", \\\"content\\\": \\\"A nested `with _lock` does not add safety\\\\u2014it causes an immediate deadlock because `threading.Lock` is non-reentrant by design. When the outer `with _lock` acquires the lock, the inner `with _lock` calls acquire() on an already-held lock from the same thread, which blocks forever. This is documented in the Python threading module. The test timeout (5s with no result) confirms the deadlock. The fix removes the duplicate acquisition; one `with _lock` is sufficient to serialise access to `account[\\\\\\\"balance\\\\\\\"]`.\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_lock = threading.Lock()\\n\\ndef transfer(account, amount):\\n    with _lock:\\n        with _lock:   # Bug: re-acquiring non-reentrant Lock causes deadlock\\n            account[\\\"balance\\\"] -= amount\\n\\n\\nAuthor says:\\nRLock vs Lock distinction and the deadlock evidence are clear. Merging.\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"done\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\nshared = {\\\"value\\\": 0}\\n_lock = threading.Lock()\\n\\ndef update(delta):\\n    with _lock:\\n        val = shared[\\\"value\\\"]\\n    # lock released here!\\n    shared[\\\"value\\\"] = val + delta   # Bug: write happens outside lock\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"inspect\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\nshared = {\\\"value\\\": 0}\\n_lock = threading.Lock()\\n\\ndef update(delta):\\n    with _lock:\\n        val = shared[\\\"value\\\"]\\n    # lock released here!\\n    shared[\\\"value\\\"] = val + delta   # Bug: write happens outside lock\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\ndef update(delta):\\n    with _lock:\\n        val = shared[\\\"value\\\"]\\n    # lock released here \\u2013 val is now stale\\n    shared[\\\"value\\\"] = val + delta   # write outside lock: race condition\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_tests\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\nshared = {\\\"value\\\": 0}\\n_lock = threading.Lock()\\n\\ndef update(delta):\\n    with _lock:\\n        val = shared[\\\"value\\\"]\\n    # lock released here!\\n    shared[\\\"value\\\"] = val + delta   # Bug: write happens outside lock\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nFAILED test_update_concurrent ... AssertionError: expected 1000, got 973\\nResults: 0 passed, 1 failed (0.0%)\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_linter\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\nshared = {\\\"value\\\": 0}\\n_lock = threading.Lock()\\n\\ndef update(delta):\\n    with _lock:\\n        val = shared[\\\"value\\\"]\\n    # lock released here!\\n    shared[\\\"value\\\"] = val + delta   # Bug: write happens outside lock\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nYour code has been rated at 8.50/10\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"query_docs\\\", \\\"content\\\": \\\"Python TOCTOU atomic read-modify-write lock\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\nshared = {\\\"value\\\": 0}\\n_lock = threading.Lock()\\n\\ndef update(delta):\\n    with _lock:\\n        val = shared[\\\"value\\\"]\\n    # lock released here!\\n    shared[\\\"value\\\"] = val + delta   # Bug: write happens outside lock\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nPython threading best practice: read AND write of shared state must occur inside the same\\nlock acquisition to form an atomic read-modify-write operation.\\nTOCTOU (time-of-check-to-time-of-use) bug: releasing the lock between read and write\\ncreates a window for another thread to modify the value.\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"fix\\\", \\\"content\\\": \\\"import threading\\\\n\\\\nshared = {\\\\\\\"value\\\\\\\": 0}\\\\n_lock = threading.Lock()\\\\n\\\\ndef fix(delta):\\\\n    with _lock:\\\\n        shared[\\\\\\\"value\\\\\\\"] += delta\\\\n\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\nshared = {\\\"value\\\": 0}\\n_lock = threading.Lock()\\n\\ndef update(delta):\\n    with _lock:\\n        val = shared[\\\"value\\\"]\\n    # lock released here!\\n    shared[\\\"value\\\"] = val + delta   # Bug: write happens outside lock\\n\\n\\nAuthor says:\\nWe acquire the lock before reading. That's thread-safe enough, isn't it?\\n\\nLast tool output:\\nFix proposed. Tests: 1/1 passed. Lint: 9/10.\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"comment\\\", \\\"content\\\": \\\"Locking only the read creates a TOCTOU (time-of-check to time-of-use) race: Thread A reads val=5, releases the lock, Thread B reads val=5, releases its lock, Thread A writes 5+delta, Thread B writes 5+delta\\\\u2014one delta is lost. Locking must encompass both the read and the write to form a single atomic operation. The Python threading docs explicitly state that read-modify-write must be serialised within one lock context. Moving the write inside `with _lock` fixes the race; the concurrent test now passes 1/1.\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\nshared = {\\\"value\\\": 0}\\n_lock = threading.Lock()\\n\\ndef update(delta):\\n    with _lock:\\n        val = shared[\\\"value\\\"]\\n    # lock released here!\\n    shared[\\\"value\\\"] = val + delta   # Bug: write happens outside lock\\n\\n\\nAuthor says:\\nTOCTOU explanation and the failing concurrent test are convincing. Merging.\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"done\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\nshared_list = []\\n\\ndef append_item(item):\\n    shared_list.append(item)\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"inspect\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\nshared_list = []\\n\\ndef append_item(item):\\n    shared_list.append(item)\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nimport threading\\nshared_list = []\\n\\ndef append_item(item):\\n    shared_list.append(item)   # list.append is GIL-safe for simple types but\\n                                # iteration + append from different threads is not safe\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_tests\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\nshared_list = []\\n\\ndef append_item(item):\\n    shared_list.append(item)\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nFAILED test_concurrent_append_and_iterate ... RuntimeError: list changed size during iteration\\nResults: 0 passed, 1 failed (0.0%)\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_linter\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\nshared_list = []\\n\\ndef append_item(item):\\n    shared_list.append(item)\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nYour code has been rated at 9.50/10\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"query_docs\\\", \\\"content\\\": \\\"Python thread-safe list append iteration concurrent RuntimeError\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\nshared_list = []\\n\\ndef append_item(item):\\n    shared_list.append(item)\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nPython docs: while list.append() is individually thread-safe due to the GIL,\\nconcurrent iteration and mutation of a list causes RuntimeError.\\nUse threading.Lock() or collections.deque (which is thread-safe for appends/pops).\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"fix\\\", \\\"content\\\": \\\"import threading\\\\nfrom collections import deque\\\\n\\\\nshared_list = deque()\\\\n_lock = threading.Lock()\\\\n\\\\ndef fix(item):\\\\n    with _lock:\\\\n        shared_list.append(item)\\\\n\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\nshared_list = []\\n\\ndef append_item(item):\\n    shared_list.append(item)\\n\\n\\nAuthor says:\\nlist.append is documented as thread-safe. Why add a lock?\\n\\nLast tool output:\\nFix proposed. Tests: 1/1 passed. Lint: 9/10.\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"comment\\\", \\\"content\\\": \\\"You are correct that a single `list.append()` call is thread-safe due to the GIL. However, the failure mode is not in the append itself\\\\u2014it is in concurrent iteration (e.g., `for item in shared_list`) while another thread appends, which mutates the list's internal length and raises RuntimeError. A lock around append serialises all mutations, preventing the size change during iteration. The Python docs recommend `collections.deque` for thread-safe producer/consumer patterns because its append and popleft are individually atomic. The test reproduces the RuntimeError and passes after the fix.\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\nshared_list = []\\n\\ndef append_item(item):\\n    shared_list.append(item)\\n\\n\\nAuthor says:\\nThe iteration safety argument and collections.deque reference are compelling. Merging.\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"done\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_running = True\\n\\ndef worker():\\n    while _running:\\n        pass\\n\\ndef stop():\\n    global _running\\n    _running = False\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"inspect\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_running = True\\n\\ndef worker():\\n    while _running:\\n        pass\\n\\ndef stop():\\n    global _running\\n    _running = False\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nimport threading\\n_running = True\\n\\ndef worker():\\n    while _running:   # Bug: CPU/interpreter may cache _running; worker may never see False\\n        pass\\n\\ndef stop():\\n    global _running\\n    _running = False\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_tests\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_running = True\\n\\ndef worker():\\n    while _running:\\n        pass\\n\\ndef stop():\\n    global _running\\n    _running = False\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nFAILED test_stop_worker ... test timed out after 3s (worker never stopped)\\n# Non-deterministic; sometimes passes on CPython but fails on PyPy/Jython\\nResults: 0 passed, 1 failed (0.0%)\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_linter\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_running = True\\n\\ndef worker():\\n    while _running:\\n        pass\\n\\ndef stop():\\n    global _running\\n    _running = False\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nW0603 volatile.py:9:4: Using the global statement (global-statement)\\nYour code has been rated at 6.50/10\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"query_docs\\\", \\\"content\\\": \\\"Python threading.Event volatile boolean flag worker stop\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_running = True\\n\\ndef worker():\\n    while _running:\\n        pass\\n\\ndef stop():\\n    global _running\\n    _running = False\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nPython threading docs: using an Event object is the recommended way to signal threads.\\nthreading.Event.set() / is_set() provide visible memory semantics across threads.\\nA plain boolean flag may be cached by the interpreter's loop; threading.Event avoids this.\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"fix\\\", \\\"content\\\": \\\"import threading\\\\n\\\\n_stop_event = threading.Event()\\\\n\\\\ndef fix_worker():\\\\n    while not _stop_event.is_set():\\\\n        pass\\\\n\\\\ndef fix_stop():\\\\n    _stop_event.set()\\\\n\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_running = True\\n\\ndef worker():\\n    while _running:\\n        pass\\n\\ndef stop():\\n    global _running\\n    _running = False\\n\\n\\nAuthor says:\\nSetting a global bool is instant. The worker thread will see it immediately.\\n\\nLast tool output:\\nFix proposed. Tests: 1/1 passed consistently. Lint: 8/10.\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"comment\\\", \\\"content\\\": \\\"On CPython with the GIL the test sometimes passes, but this is not guaranteed. On PyPy, Jython, or in tight loops CPython can optimise away repeated global reads and the worker never observes the updated flag. The Python threading documentation explicitly recommends `threading.Event` for stop signals because its `set()` and `is_set()` use internal synchronisation that guarantees visibility across threads. Plain boolean globals have no such guarantee. The fix uses threading.Event; the test now passes consistently (10/10 runs).\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_running = True\\n\\ndef worker():\\n    while _running:\\n        pass\\n\\ndef stop():\\n    global _running\\n    _running = False\\n\\n\\nAuthor says:\\nthreading.Event is the documented pattern. Evidence and docs are solid. Merging.\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"done\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\nlock_a = threading.Lock()\\nlock_b = threading.Lock()\\n\\ndef task1():\\n    with lock_a:\\n        with lock_b:\\n            pass\\n\\ndef task2():\\n    with lock_b:       # Bug: lock acquisition order differs from task1 -> deadlock\\n        with lock_a:\\n            pass\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"inspect\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\nlock_a = threading.Lock()\\nlock_b = threading.Lock()\\n\\ndef task1():\\n    with lock_a:\\n        with lock_b:\\n            pass\\n\\ndef task2():\\n    with lock_b:       # Bug: lock acquisition order differs from task1 -> deadlock\\n        with lock_a:\\n            pass\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\ntask1 acquires: lock_a then lock_b\\ntask2 acquires: lock_b then lock_a  \\u2190 inverted order \\u2192 classic AB/BA deadlock\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_tests\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\nlock_a = threading.Lock()\\nlock_b = threading.Lock()\\n\\ndef task1():\\n    with lock_a:\\n        with lock_b:\\n            pass\\n\\ndef task2():\\n    with lock_b:       # Bug: lock acquisition order differs from task1 -> deadlock\\n        with lock_a:\\n            pass\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nFAILED test_concurrent_tasks ... test timed out after 5s (deadlock detected)\\nResults: 0 passed, 1 failed (0.0%)\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_linter\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\nlock_a = threading.Lock()\\nlock_b = threading.Lock()\\n\\ndef task1():\\n    with lock_a:\\n        with lock_b:\\n            pass\\n\\ndef task2():\\n    with lock_b:       # Bug: lock acquisition order differs from task1 -> deadlock\\n        with lock_a:\\n            pass\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nYour code has been rated at 9.00/10\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"query_docs\\\", \\\"content\\\": \\\"deadlock prevention lock ordering Coffman conditions Python threading\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\nlock_a = threading.Lock()\\nlock_b = threading.Lock()\\n\\ndef task1():\\n    with lock_a:\\n        with lock_b:\\n            pass\\n\\ndef task2():\\n    with lock_b:       # Bug: lock acquisition order differs from task1 -> deadlock\\n        with lock_a:\\n            pass\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nLock-ordering discipline (Coffman conditions): to prevent deadlock, all threads must acquire\\nmultiple locks in the same global order. If task1 acquires A then B, task2 must also acquire A then B.\\nPython threading docs recommend lock hierarchies to avoid circular wait.\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"fix\\\", \\\"content\\\": \\\"import threading\\\\n\\\\nlock_a = threading.Lock()\\\\nlock_b = threading.Lock()\\\\n\\\\ndef fix_task1():\\\\n    with lock_a:\\\\n        with lock_b:\\\\n            pass\\\\n\\\\ndef fix_task2():\\\\n    with lock_a:       # Same order as task1: A then B\\\\n        with lock_b:\\\\n            pass\\\\n\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\nlock_a = threading.Lock()\\nlock_b = threading.Lock()\\n\\ndef task1():\\n    with lock_a:\\n        with lock_b:\\n            pass\\n\\ndef task2():\\n    with lock_b:       # Bug: lock acquisition order differs from task1 -> deadlock\\n        with lock_a:\\n            pass\\n\\n\\nAuthor says:\\nDeadlocks are rare in practice. The tests are just unlucky with thread scheduling.\\n\\nLast tool output:\\nFix proposed. Tests: consistently 10/10 passed. Lint: 9/10.\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"comment\\\", \\\"content\\\": \\\"The AB/BA deadlock is not a scheduling anomaly\\\\u2014it is a structural guarantee of the Coffman circular-wait condition. When task1 holds lock_a and waits for lock_b while task2 holds lock_b and waits for lock_a, both threads are permanently blocked. This can be reproduced deterministically by inserting a sleep between the two acquisitions. The lock-ordering discipline (acquire all locks in a consistent global order) is a foundational concurrency principle documented in both OS textbooks and the Python threading docs. Changing task2 to acquire A then B eliminates the circular wait. Tests pass consistently (10/10) after the fix.\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\nlock_a = threading.Lock()\\nlock_b = threading.Lock()\\n\\ndef task1():\\n    with lock_a:\\n        with lock_b:\\n            pass\\n\\ndef task2():\\n    with lock_b:       # Bug: lock acquisition order differs from task1 -> deadlock\\n        with lock_a:\\n            pass\\n\\n\\nAuthor says:\\nLock-ordering discipline is a proven engineering principle. Merging.\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"done\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_outer = threading.Lock()\\n_inner = threading.Lock()\\n\\ndef critical_section():\\n    _outer.acquire()\\n    _inner.acquire()        # Bug: no timeout \\u2013 will block forever if inner is held\\n    try:\\n        pass\\n    finally:\\n        _inner.release()\\n        _outer.release()\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"inspect\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_outer = threading.Lock()\\n_inner = threading.Lock()\\n\\ndef critical_section():\\n    _outer.acquire()\\n    _inner.acquire()        # Bug: no timeout \\u2013 will block forever if inner is held\\n    try:\\n        pass\\n    finally:\\n        _inner.release()\\n        _outer.release()\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\ndef critical_section():\\n    _outer.acquire()\\n    _inner.acquire()   # No timeout: if _inner is held elsewhere, this blocks indefinitely\\n    # Correct pattern: use timeout and release outer if inner cannot be acquired\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_tests\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_outer = threading.Lock()\\n_inner = threading.Lock()\\n\\ndef critical_section():\\n    _outer.acquire()\\n    _inner.acquire()        # Bug: no timeout \\u2013 will block forever if inner is held\\n    try:\\n        pass\\n    finally:\\n        _inner.release()\\n        _outer.release()\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nFAILED test_nested_lock_timeout ... test timed out after 5s\\nResults: 0 passed, 1 failed (0.0%)\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_linter\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_outer = threading.Lock()\\n_inner = threading.Lock()\\n\\ndef critical_section():\\n    _outer.acquire()\\n    _inner.acquire()        # Bug: no timeout \\u2013 will block forever if inner is held\\n    try:\\n        pass\\n    finally:\\n        _inner.release()\\n        _outer.release()\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nW0107 nested_lock.py:7:4: acquire without context manager (consider-using-with)\\nYour code has been rated at 6.00/10\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"query_docs\\\", \\\"content\\\": \\\"Python threading Lock acquire timeout nested deadlock\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_outer = threading.Lock()\\n_inner = threading.Lock()\\n\\ndef critical_section():\\n    _outer.acquire()\\n    _inner.acquire()        # Bug: no timeout \\u2013 will block forever if inner is held\\n    try:\\n        pass\\n    finally:\\n        _inner.release()\\n        _outer.release()\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nPython threading.Lock.acquire(timeout): if timeout > 0, blocks at most timeout seconds,\\nreturns True on success or False on failure. Always release already-held locks if a\\nsubsequent acquire fails to avoid resource leaks and deadlocks.\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"fix\\\", \\\"content\\\": \\\"import threading\\\\n\\\\n_outer = threading.Lock()\\\\n_inner = threading.Lock()\\\\n\\\\ndef fix():\\\\n    if not _outer.acquire(timeout=1):\\\\n        raise RuntimeError(\\\\\\\"Could not acquire outer lock\\\\\\\")\\\\n    try:\\\\n        if not _inner.acquire(timeout=1):\\\\n            _outer.release()\\\\n            raise RuntimeError(\\\\\\\"Could not acquire inner lock\\\\\\\")\\\\n        try:\\\\n            pass  # critical section\\\\n        finally:\\\\n            _inner.release()\\\\n    finally:\\\\n        _outer.release()\\\\n\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_outer = threading.Lock()\\n_inner = threading.Lock()\\n\\ndef critical_section():\\n    _outer.acquire()\\n    _inner.acquire()        # Bug: no timeout \\u2013 will block forever if inner is held\\n    try:\\n        pass\\n    finally:\\n        _inner.release()\\n        _outer.release()\\n\\n\\nAuthor says:\\nTimeouts make the code more complex. Why not just let it block?\\n\\nLast tool output:\\nFix proposed. Tests: 1/1 passed. Lint: 8/10.\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"comment\\\", \\\"content\\\": \\\"Blocking indefinitely means a single thread holding `_inner` (e.g., due to an exception or slow I/O) causes the entire system to freeze. The timeout approach adds complexity but provides liveness: if the inner lock cannot be acquired in 1 second, the outer lock is released and a RuntimeError is raised, allowing the caller to retry or degrade gracefully. This follows the Python threading documentation's recommendation for `acquire(timeout=N)`. The test simulates inner-lock contention and confirms the fix terminates cleanly instead of hanging.\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_outer = threading.Lock()\\n_inner = threading.Lock()\\n\\ndef critical_section():\\n    _outer.acquire()\\n    _inner.acquire()        # Bug: no timeout \\u2013 will block forever if inner is held\\n    try:\\n        pass\\n    finally:\\n        _inner.release()\\n        _outer.release()\\n\\n\\nAuthor says:\\nTimeout with graceful rollback is documented best practice. All tests pass. Merging.\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"done\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\nresults = []\\n\\ndef worker(i):\\n    results.append(i * i)\\n\\ndef run_all():\\n    threads = [threading.Thread(target=worker, args=(i,)) for i in range(10)]\\n    for t in threads:\\n        t.start()\\n    return results   # Bug: returns before threads finish; results may be incomplete\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"inspect\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\nresults = []\\n\\ndef worker(i):\\n    results.append(i * i)\\n\\ndef run_all():\\n    threads = [threading.Thread(target=worker, args=(i,)) for i in range(10)]\\n    for t in threads:\\n        t.start()\\n    return results   # Bug: returns before threads finish; results may be incomplete\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\ndef run_all():\\n    ...\\n    for t in threads:\\n        t.start()\\n    return results   # Missing join(): main thread returns before workers complete\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_tests\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\nresults = []\\n\\ndef worker(i):\\n    results.append(i * i)\\n\\ndef run_all():\\n    threads = [threading.Thread(target=worker, args=(i,)) for i in range(10)]\\n    for t in threads:\\n        t.start()\\n    return results   # Bug: returns before threads finish; results may be incomplete\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nFAILED test_run_all_complete ... AssertionError: expected 10 results, got 3\\n# Non-deterministic; count varies per run\\nResults: 0 passed, 1 failed (0.0%)\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_linter\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\nresults = []\\n\\ndef worker(i):\\n    results.append(i * i)\\n\\ndef run_all():\\n    threads = [threading.Thread(target=worker, args=(i,)) for i in range(10)]\\n    for t in threads:\\n        t.start()\\n    return results   # Bug: returns before threads finish; results may be incomplete\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nYour code has been rated at 8.50/10\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"query_docs\\\", \\\"content\\\": \\\"Python threading join() fork-join pattern wait thread termination\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\nresults = []\\n\\ndef worker(i):\\n    results.append(i * i)\\n\\ndef run_all():\\n    threads = [threading.Thread(target=worker, args=(i,)) for i in range(10)]\\n    for t in threads:\\n        t.start()\\n    return results   # Bug: returns before threads finish; results may be incomplete\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nPython threading.Thread.join(): \\\"Wait until the thread terminates.\\\"\\nPattern: start all threads, then join all threads before reading shared state.\\nWithout join(), the main thread may read partial results.\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"fix\\\", \\\"content\\\": \\\"import threading\\\\n\\\\nresults = []\\\\n\\\\ndef worker(i):\\\\n    results.append(i * i)\\\\n\\\\ndef fix():\\\\n    threads = [threading.Thread(target=worker, args=(i,)) for i in range(10)]\\\\n    for t in threads:\\\\n        t.start()\\\\n    for t in threads:\\\\n        t.join()    # Wait for all workers to finish\\\\n    return results\\\\n\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\nresults = []\\n\\ndef worker(i):\\n    results.append(i * i)\\n\\ndef run_all():\\n    threads = [threading.Thread(target=worker, args=(i,)) for i in range(10)]\\n    for t in threads:\\n        t.start()\\n    return results   # Bug: returns before threads finish; results may be incomplete\\n\\n\\nAuthor says:\\nWe use a sleep(1) before returning in production. That's enough, right?\\n\\nLast tool output:\\nFix proposed. Tests: consistently 1/1 passed. Lint: 9/10.\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"comment\\\", \\\"content\\\": \\\"A `sleep(1)` is a heuristic that may fail under load (threads take >1s) or waste time when threads finish in milliseconds. `thread.join()` is the deterministic synchronisation mechanism documented in Python's threading module: it blocks the caller until the thread terminates, with zero guessing. The fork-join pattern (start all, then join all) is the canonical way to wait for worker threads. The fix removes the race between the return statement and the still-running workers, making the result count always equal to 10.\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\nresults = []\\n\\ndef worker(i):\\n    results.append(i * i)\\n\\ndef run_all():\\n    threads = [threading.Thread(target=worker, args=(i,)) for i in range(10)]\\n    for t in threads:\\n        t.start()\\n    return results   # Bug: returns before threads finish; results may be incomplete\\n\\n\\nAuthor says:\\njoin() is the documented correct synchronisation. No sleep needed. Merging.\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"done\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_lock = threading.Lock()\\n\\ndef risky_operation():\\n    _lock.acquire()\\n    if some_condition():\\n        return   # Bug: lock never released on this path\\n    _lock.release()\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"inspect\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_lock = threading.Lock()\\n\\ndef risky_operation():\\n    _lock.acquire()\\n    if some_condition():\\n        return   # Bug: lock never released on this path\\n    _lock.release()\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\ndef risky_operation():\\n    _lock.acquire()\\n    if some_condition():\\n        return   # Early return leaks the lock; all subsequent callers deadlock\\n    _lock.release()\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_tests\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_lock = threading.Lock()\\n\\ndef risky_operation():\\n    _lock.acquire()\\n    if some_condition():\\n        return   # Bug: lock never released on this path\\n    _lock.release()\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nFAILED test_risky_operation_condition_true ... subsequent call hangs (deadlock)\\nPASSED test_risky_operation_condition_false\\nResults: 1 passed, 1 failed (50.0%)\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_linter\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_lock = threading.Lock()\\n\\ndef risky_operation():\\n    _lock.acquire()\\n    if some_condition():\\n        return   # Bug: lock never released on this path\\n    _lock.release()\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nW0107 mutex_release.py:6:4: Lock acquired but not always released (missing-finally)\\nYour code has been rated at 5.00/10\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"query_docs\\\", \\\"content\\\": \\\"Python threading lock release all paths try finally context manager\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_lock = threading.Lock()\\n\\ndef risky_operation():\\n    _lock.acquire()\\n    if some_condition():\\n        return   # Bug: lock never released on this path\\n    _lock.release()\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nPython threading docs: use `with lock:` context manager or try/finally to guarantee\\nlock release on all code paths, including early returns and exceptions.\\nFailing to release a lock causes all subsequent acquire() calls to block indefinitely.\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"fix\\\", \\\"content\\\": \\\"import threading\\\\n\\\\n_lock = threading.Lock()\\\\n\\\\ndef fix():\\\\n    with _lock:\\\\n        if some_condition():\\\\n            return\\\\n        # rest of operation\\\\n\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_lock = threading.Lock()\\n\\ndef risky_operation():\\n    _lock.acquire()\\n    if some_condition():\\n        return   # Bug: lock never released on this path\\n    _lock.release()\\n\\n\\nAuthor says:\\nThe release is there for the normal path. The conditional path is just an early exit.\\n\\nLast tool output:\\nFix proposed. Tests: 2/2 passed. Lint: 9/10.\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"comment\\\", \\\"content\\\": \\\"An early return is not just an exit\\\\u2014it is an exit that bypasses the `_lock.release()` call on line 8. Once the lock is left acquired, every subsequent call to `_lock.acquire()` blocks forever because the lock is never available. This is the lock-leak pattern flagged by pylint as 'missing-finally'. The `with _lock:` context manager guarantees release on every exit path\\\\u2014normal return, early return, and exception\\\\u2014because it calls `__exit__` unconditionally. Therefore the `with` pattern is the correct and idiomatic fix. Both tests pass after the change.\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_lock = threading.Lock()\\n\\ndef risky_operation():\\n    _lock.acquire()\\n    if some_condition():\\n        return   # Bug: lock never released on this path\\n    _lock.release()\\n\\n\\nAuthor says:\\ntry/finally and context manager patterns guarantee release on all paths. Merging.\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"done\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_instance = None\\n\\ndef get_instance():\\n    global _instance\\n    if _instance is None:\\n        _instance = ExpensiveObject()   # Bug: two threads can both see None and create two instances\\n    return _instance\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"inspect\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_instance = None\\n\\ndef get_instance():\\n    global _instance\\n    if _instance is None:\\n        _instance = ExpensiveObject()   # Bug: two threads can both see None and create two instances\\n    return _instance\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\ndef get_instance():\\n    global _instance\\n    if _instance is None:\\n        _instance = ExpensiveObject()   # Check-then-act without lock: two threads may init twice\\n    return _instance\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_tests\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_instance = None\\n\\ndef get_instance():\\n    global _instance\\n    if _instance is None:\\n        _instance = ExpensiveObject()   # Bug: two threads can both see None and create two instances\\n    return _instance\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nFAILED test_singleton_concurrent ... AssertionError: id(inst1) != id(inst2)\\n# Two different instances created\\nResults: 0 passed, 1 failed (0.0%)\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"run_linter\\\", \\\"content\\\": \\\"\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_instance = None\\n\\ndef get_instance():\\n    global _instance\\n    if _instance is None:\\n        _instance = ExpensiveObject()   # Bug: two threads can both see None and create two instances\\n    return _instance\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nW0603 singleton.py:7:4: Using the global statement (global-statement)\\nYour code has been rated at 6.50/10\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"query_docs\\\", \\\"content\\\": \\\"Python double-checked locking singleton thread-safe GIL init race\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_instance = None\\n\\ndef get_instance():\\n    global _instance\\n    if _instance is None:\\n        _instance = ExpensiveObject()   # Bug: two threads can both see None and create two instances\\n    return _instance\\n\\n\\nAuthor says:\\n(no response yet \\u2013 start with inspection)\\n\\nLast tool output:\\nPython threading: double-checked locking requires a lock around both the check and the init.\\nPattern: acquire lock, re-check, init if still None, release.\\nAlternatively, use a module-level singleton (module import is thread-safe in CPython).\\n\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"fix\\\", \\\"content\\\": \\\"import threading\\\\n\\\\n_instance = None\\\\n_lock = threading.Lock()\\\\n\\\\ndef fix():\\\\n    global _instance\\\\n    if _instance is None:\\\\n        with _lock:\\\\n            if _instance is None:   # Double-checked locking: re-check inside lock\\\\n                _instance = object()  # Replace with ExpensiveObject()\\\\n    return _instance\\\\n\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_instance = None\\n\\ndef get_instance():\\n    global _instance\\n    if _instance is None:\\n        _instance = ExpensiveObject()   # Bug: two threads can both see None and create two instances\\n    return _instance\\n\\n\\nAuthor says:\\nPython's GIL means only one thread runs at a time. The double-init can't happen.\\n\\nLast tool output:\\nFix proposed. Tests: consistently 1/1 passed. Lint: 8/10.\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"comment\\\", \\\"content\\\": \\\"The GIL allows the interpreter to switch threads between any two bytecodes. The check `if _instance is None` and the assignment `_instance = ExpensiveObject()` are separate bytecode sequences. A context switch between them allows Thread B to see `_instance is None` as True and create a second instance. Our test reproduces this with 50 concurrent threads\\\\u2014two distinct object IDs were observed. The double-checked locking pattern (check outside lock for performance, re-check inside lock for correctness) is the documented solution. After the fix, all 50 threads receive the same instance ID (test passes 10/10 runs).\\\"}\"\n  },\n  {\n    \"prompt\": \"You are an AI code review agent. Your goal is to convince a simulated human developer to accept your proposed fix and name your proposed fix function fix.\\n\\nThe developer has a **defensive** personality and will only accept if you provide solid evidence:\\n- Tests pass (high pass ratio)\\n- Lint is clean (zero errors)\\n- Documentation or references are provided\\n- Your reasoning is clear, uses words like \\\"because\\\" or \\\"therefore\\\", and is detailed (over 30 words if needed)\\n\\nWorkflow:\\n1. Use `inspect` to understand the code.\\n2. Use `run_tests` and `run_linter` to gather evidence.\\n3. Use `query_docs` when you need references or language-specific guidance.\\n4. Propose a fix (`fix`) and explain why it works (`comment` or `question`).\\n5. If the developer pushes back, read their response carefully and address their specific concern.\\n6. Once convinced, use `done` to finish.\\n\\nCode:\\nimport threading\\n\\n_instance = None\\n\\ndef get_instance():\\n    global _instance\\n    if _instance is None:\\n        _instance = ExpensiveObject()   # Bug: two threads can both see None and create two instances\\n    return _instance\\n\\n\\nAuthor says:\\nDouble-checked locking with evidence and docs is conclusive. Will merge.\\n\\nLast tool output:\\n(none)\\n\\nAvailable actions:\\nrun_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\nRespond ONLY in JSON:\\n{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}\",\n    \"action\": \"{\\\"action_type\\\": \\\"done\\\", \\\"content\\\": \\\"\\\"}\"\n  }\n]\n\nwith open(\"training_data.json\", \"w\") as f:\n    json.dump(dummy_training_data, f, indent=2)\n\nprint(\"Created training_data.json\")\n\n# environment.py \u2013 FULLY CORRECTED RL Environment (TRUE Markov + Fixed Bugs)\n\nimport sys\nimport subprocess\nimport tempfile\nimport os\nimport re\nfrom dataclasses import dataclass, field\nfrom typing import Tuple, Dict, Any, Optional, List\n\n#from models import (\n#    AnyAction, WriteComment, ProposeFix, Execute, Inspect,\n#    RunLinter, RunTests, QueryDocs, Skip, Done, AskQuestion,\n#    Observation, Reward, State\n#)\n#from redteam import RedTeam\n#from test_runner import TestRunner\n#from author import PersonaAuthor\n#from rltool import ToolBox\n#from rubrics import (\n#    ToolUsageRubric,\n#    TestDeltaRubric,\n#    LintDeltaRubric,\n#    TerminalSuccessRubric,\n#    ExplorationRubric,\n#    AntiHackingRubric,\n#    StepPenaltyRubric,\n#)\n\n# ======================================================================\n# FULLY MARKOV OBSERVATION (NOTHING HIDDEN)\n# ======================================================================\n\n\n@dataclass\nclass EnhancedObservation:\n    code_snippet: str\n    last_tool_output: str\n\n    current_test_score: float\n    current_lint_score: float\n    negotiation_score: float\n\n    previous_test_score: float\n    previous_lint_score: float\n\n    author_confidence: float\n    author_threshold: float\n\n    step: int\n    max_steps: int\n    progress_ratio: float\n\n    tests_run: bool\n    linter_run: bool\n    docs_queried: bool\n\n    last_action_type: str\n    action_history: List[str]\n\n    done: bool\n\n    bug_description: str\n    comments_count: int\n\n    # default fields must be at the very end\n    author_response: str = \"\"\n\n# ======================================================================\n# HELPER FUNCTIONS\n# ======================================================================\ndef execute_code(code: str, timeout_sec: int = 5) -> Tuple[bool, str, str]:\n    if not code.strip():\n        return False, \"\", \"Error: Empty code\"\n\n    with tempfile.NamedTemporaryFile(mode='w', suffix='.py', delete=False, encoding='utf-8') as f:\n        f.write(code)\n        tmp_path = f.name\n\n\n    try:\n        result = subprocess.run(\n            [sys.executable, tmp_path],\n            capture_output=True,\n            text=True,\n            timeout=timeout_sec\n        )\n        success = (result.returncode == 0)\n        return success, result.stdout, result.stderr\n    except subprocess.TimeoutExpired:\n        return False, \"\", f\"Timeout after {timeout_sec}s\"\n    except Exception as e:\n        return False, \"\", f\"Execution error: {str(e)}\"\n    finally:\n        try:\n            os.unlink(tmp_path)\n        except:\n            pass\n\n\n# ======================================================================\n# ENHANCED CODE REVIEW ENVIRONMENT\n# ======================================================================\n@dataclass\nclass CodeReviewEnv:\n    task: str = \"easy\"\n    max_steps: int = 10\n    step_penalty: float = 0.01\n    reward_profile: str = \"full\"  # \"full\" or \"core\"\n\n    # Curriculum learning\n    auto_difficulty: bool = False\n    success_threshold: float = 0.7\n\n    # Reward shaping parameters\n    delta_weight: float = 0.3\n    tool_usage_bonus: float = 0.05\n    diversity_bonus: float = 0.03\n\n    _red_team: Optional[RedTeam] = field(init=False, default=None)\n    _author: Optional[PersonaAuthor] = field(init=False, default=None)\n\n    _current_code: str = field(init=False, default=\"\")\n    _current_bug_id: str = field(init=False, default=\"\")\n    _bug_description: str = field(init=False, default=\"\")\n    _oracle_fix: str = field(init=False, default=\"\")\n\n    _comments: list = field(init=False, default_factory=list)\n    _test_results: Optional[str] = field(init=False, default=None)\n    _lint_results: Optional[str] = field(init=False, default=None)\n    _doc_results: Optional[str] = field(init=False, default=None)\n\n    _step_count: int = field(init=False, default=0)\n    _done: bool = field(init=False, default=False)\n\n    # State tracking for dense rewards\n    _previous_test_score: float = field(init=False, default=0.0)\n    _previous_lint_score: float = field(init=False, default=0.0)\n    _current_test_score: float = field(init=False, default=0.0)\n    _current_lint_score: float = field(init=False, default=0.0)\n\n    # Tool usage tracking\n    _tests_run: bool = field(init=False, default=False)\n    _linter_run: bool = field(init=False, default=False)\n    _docs_queried: bool = field(init=False, default=False)\n\n    # Action history\n    _action_history: List[str] = field(init=False, default_factory=list)\n    _last_action_type: str = field(init=False, default=\"none\")\n    _last_author_response: str = field(init=False, default=\"\")\n\n    # FIXED: Track CUMULATIVE episode reward\n    _episode_total_reward: float = field(init=False, default=0.0)\n    _episode_rewards: List[float] = field(init=False, default_factory=list)\n    _difficulty_level: int = field(init=False, default=0)\n\n    # Bug-id bridge:\n    # RedTeam has fine-grained IDs, while TestRunner currently expects a\n    # smaller canonical set. Keep this mapping here so both modules can evolve\n    # independently without breaking evaluation.\n    _BUG_ID_CANONICAL_MAP = {\n        # Easy-family\n        \"simple_typo\": \"null_check\",\n        \"default_value\": \"null_check\",\n        \"empty_return\": \"null_check\",\n        \"string_index\": \"off_by_one\",\n\n        # Medium-family\n        \"loop_skip\": \"off_by_one\",\n        \"sign_error\": \"wrong_operator\",\n        \"swap_args\": \"wrong_operator\",\n        \"uninitialised_var\": \"null_check\",\n\n        # Hard-family\n        \"division_by_zero_empty\": \"division_by_zero\",\n        \"division_by_zero_zero\": \"division_by_zero\",\n        \"float_precision\": \"division_by_zero\",\n        \"abs_usage\": \"division_by_zero\",\n        \"round_error\": \"division_by_zero\",\n    }\n\n    # ===================================================================\n    def __post_init__(self):\n        self.set_task(self.task)\n\n    # ===================================================================\n    def _build_rubrics(self):\n        \"\"\"\n        Build rubric stack from a named reward profile.\n        - full: richer shaping for exploration/tool-use behavior\n        - core: minimal stable signal for quick ablations/baselines\n        \"\"\"\n        core_rubrics = [\n            TestDeltaRubric(weight=self.delta_weight),\n            LintDeltaRubric(weight=self.delta_weight),\n            TerminalSuccessRubric(),\n            StepPenaltyRubric(penalty=self.step_penalty),\n        ]\n        if self.reward_profile == \"core\":\n            return core_rubrics\n        if self.reward_profile == \"full\":\n            return [\n                *core_rubrics[:-1],  # step penalty appended at end for consistent ordering\n                ToolUsageRubric(bonus=self.tool_usage_bonus),\n                ExplorationRubric(penalty=-0.05, bonus=self.diversity_bonus * 0.7),\n                AntiHackingRubric(),\n                core_rubrics[-1],\n            ]\n        raise ValueError(f\"Unknown reward_profile: {self.reward_profile}\")\n\n    # ===================================================================\n    def set_task(self, task: str):\n        if task not in [\"easy\", \"medium\", \"hard\", \"harder\", \"hardest\"]:\n            raise ValueError(f\"Unknown task: {task}\")\n\n        self.task = task\n        # Use stochastic bug sampling across episodes; fixed seed here would\n        # repeatedly select the same bug and weaken training diversity.\n        self._red_team = RedTeam(task, seed=None)\n        self._author = PersonaAuthor()\n        self.rubrics = self._build_rubrics()\n\n        task_to_level = {\n            \"easy\": 0, \"medium\": 1, \"hard\": 2,\n            \"harder\": 3, \"hardest\": 4\n        }\n        self._difficulty_level = task_to_level[task]\n\n        self._reset_internal()\n\n    # ===================================================================\n    def _reset_internal(self):\n        self._step_count = 0                         # \u2190 FIXED\n        self._comments = []\n        self._test_results = None\n        self._lint_results = None\n        self._doc_results = None\n        self._done = False\n\n        # Reset state tracking\n        self._previous_test_score = 0.0\n        self._previous_lint_score = 0.0\n        self._current_test_score = 0.0\n        self._current_lint_score = 0.0\n\n        self._tests_run = False\n        self._linter_run = False\n        self._docs_queried = False\n\n        self._action_history = []\n        self._last_action_type = \"none\"\n        self._last_author_response = \"\"\n\n        # FIXED: Reset episode cumulative reward\n        self._episode_total_reward = 0.0\n\n        self._author.reset()\n\n        # Base tasks\n        if self.task == \"easy\":\n            original = \"def get_user(id):\\n    if id in users:\\n        return users[id]\"\n        elif self.task == \"medium\":\n            original = \"def process_items(items):\\n    for item in items:\\n        print(item)\"\n        elif self.task == \"hard\":\n            original = \"def average(data):\\n    if not data:\\n        return 0\\n    return sum(data) / len(data)\"\n        elif self.task == \"harder\":\n            original = \"counter = 0\\ndef increment():\\n    global counter\\n    with lock:\\n        counter += 1\"\n        else:\n            original = \"def safe_work():\\n    with lock1:\\n        with lock2:\\n            do_work()\"\n\n        buggy_code, bug_id, desc, oracle = self._red_team.inject_bug(original)\n        self._current_code = buggy_code\n        self._current_bug_id = bug_id\n        self._bug_description = desc\n        self._oracle_fix = oracle\n        self._comments.append(f\"[RedTeam] {desc}\")\n\n    # ===================================================================\n    def reset(self) -> EnhancedObservation:\n        \"\"\"Reset with optional curriculum adjustment.\"\"\"\n        if self.auto_difficulty and len(self._episode_rewards) > 0:\n            recent_performance = sum(self._episode_rewards[-5:]) / min(5, len(self._episode_rewards))\n\n            if recent_performance > self.success_threshold and self._difficulty_level < 4:\n                self._difficulty_level += 1\n                print(f\"[Curriculum] Increasing difficulty to level {self._difficulty_level}\")\n            elif recent_performance < 0.3 and self._difficulty_level > 0:\n                self._difficulty_level -= 1\n                print(f\"[Curriculum] Decreasing difficulty to level {self._difficulty_level}\")\n\n            level_to_task = {0: \"easy\", 1: \"medium\", 2: \"hard\", 3: \"harder\", 4: \"hardest\"}\n            self.task = level_to_task[self._difficulty_level]\n            # Keep curriculum stochastic for better coverage within each level.\n            self._red_team = RedTeam(self.task, seed=None)\n\n        self._reset_internal()\n        return self._get_observation()\n\n    # ===================================================================\n    def _get_observation(self) -> EnhancedObservation:\n        \"\"\"Return COMPLETE Markov state.\"\"\"\n        # Keep the author's message separate from tool output.\n        # Using `_test_results` here can leak unrelated outputs (tests/linter/docs)\n        # and gives the policy a noisy signal for dialogue actions.\n        if self._last_action_type in (\"comment\", \"question\", \"fix\"):\n            author_response = self._last_author_response\n        else:\n            author_response = \"\"\n\n        return EnhancedObservation(\n            code_snippet=self._current_code,\n            last_tool_output=self._test_results or \"\",\n            author_response=author_response,          # \u2190 now field exists\n\n            current_test_score=self._current_test_score,\n            current_lint_score=self._current_lint_score,\n            negotiation_score=self._author.get_negotiation_score(),\n\n            previous_test_score=self._previous_test_score,\n            previous_lint_score=self._previous_lint_score,\n\n            author_confidence=self._author._confidence,\n            author_threshold=self._author.thresholds.get(self._author.personality, 0.5),\n\n            step=self._step_count,\n            max_steps=self.max_steps,\n            # Guard against accidental `max_steps=0` configs.\n            progress_ratio=(self._step_count / self.max_steps) if self.max_steps > 0 else 1.0,\n\n            tests_run=self._tests_run,\n            linter_run=self._linter_run,\n            docs_queried=self._docs_queried,\n\n            last_action_type=self._last_action_type,\n            action_history=self._action_history[-5:],\n\n            done=self._done,\n\n            bug_description=self._bug_description,\n            comments_count=len(self._comments),\n        )\n\n    # ===================================================================\n    def _get_action_type(self, action: AnyAction) -> str:\n        \"\"\"Extract action type as string.\"\"\"\n        if isinstance(action, RunTests):\n            return \"run_tests\"\n        elif isinstance(action, RunLinter):\n            return \"run_linter\"\n        elif isinstance(action, QueryDocs):\n            return \"query_docs\"\n        elif isinstance(action, Execute):\n            return \"execute\"\n        elif isinstance(action, Inspect):\n            return \"inspect\"\n        elif isinstance(action, WriteComment):\n            return \"comment\"\n        elif isinstance(action, AskQuestion):\n            return \"question\"\n        elif isinstance(action, ProposeFix):\n            return \"fix\"\n        elif isinstance(action, Done):\n            return \"done\"\n        elif isinstance(action, Skip):\n            return \"skip\"\n        else:\n            return \"unknown\"\n\n    # ===================================================================\n    def _get_test_runner_bug_id(self) -> str:\n        \"\"\"\n        Normalize RedTeam bug ids to the canonical ids understood by TestRunner.\n        Falls back to the original id for known direct matches.\n        \"\"\"\n        return self._BUG_ID_CANONICAL_MAP.get(self._current_bug_id, self._current_bug_id)\n\n    # ===================================================================\n    def step(self, action: AnyAction) -> Tuple[EnhancedObservation, Reward, bool, Dict[str, Any]]:\n        \"\"\"\n        TRUE RL STEP with:\n        - Complete Markov observations (no hidden state)\n        - Dense intermediate rewards\n        - Delta-based credit assignment (no double-counting)\n        - Proper episode reward tracking\n        \"\"\"\n        if self._done:\n            raise RuntimeError(\"Episode already finished\")\n\n        # Store previous metrics for delta computation\n        self._previous_test_score = self._current_test_score\n        self._previous_lint_score = self._current_lint_score\n        # Snapshot tool-usage flags BEFORE action mutates them.\n        # Rubrics use these to detect true \"first-use\" behavior.\n        prev_tests_run = self._tests_run\n        prev_linter_run = self._linter_run\n        prev_docs_queried = self._docs_queried\n\n        base_reward = 0.0\n        action_type = self._get_action_type(action)\n\n        # Update action history\n        self._action_history.append(action_type)\n        self._last_action_type = action_type\n\n        # ==============================================================\n        # TOOL ACTIONS\n        # ==============================================================\n        if isinstance(action, Execute):\n            success, stdout, stderr = execute_code(self._current_code)\n            output = (stdout + stderr).strip() or \"No output\"\n            self._test_results = f\"[Execute] {'Success' if success else 'Failed'}\\n{output[:300]}\"\n            base_reward = 0.001 if success else -0.05\n\n        elif isinstance(action, Inspect):\n            self._test_results = f\"[Inspect]\\n{self._current_code[:500]}\"\n            base_reward = 0.001\n\n        elif isinstance(action, RunLinter):\n            lint_output = ToolBox.run_linter(self._current_code)\n            self._lint_results = lint_output[:500]\n            self._test_results = f\"[Linter]\\n{self._lint_results}\"\n\n            self._current_lint_score = self._run_linter_score(self._current_code)\n            self._linter_run = True\n            base_reward = 0.002\n\n        elif isinstance(action, RunTests):\n            runner = TestRunner(self._get_test_runner_bug_id())\n            score, output = runner.run_tests(self._current_code)\n\n            self._current_test_score = score\n            self._tests_run = True\n\n            self._test_results = f\"[Tests] Score: {score:.2f}\\n{output[:300]}\"\n            base_reward = 0.002\n\n            if score > 0.8:\n                base_reward += 0.005\n\n        elif isinstance(action, QueryDocs):\n            # Normalize query to avoid rewarding empty/noisy requests.\n            query_topic = (action.query_topic or \"\").strip()\n            doc = ToolBox.query_docs(query_topic if query_topic else \"general bug fixing\")\n            self._doc_results = doc\n            self._test_results = f\"[Docs]\\n{doc[:400]}\"\n            self._docs_queried = True\n            base_reward = 0.001\n\n        # ==============================================================\n        # COMMUNICATION ACTIONS\n        # ==============================================================\n        elif isinstance(action, WriteComment):\n            self._comments.append(f\"Agent: {action.comment_text}\")\n\n            response = self._author.respond(\n                agent_comment=action.comment_text,\n                test_results=self._test_results,\n                lint_results=self._lint_results,\n                doc_results=self._doc_results,\n                proposed_fix=None,\n                original_code=self._current_code\n            )\n\n            self._comments.append(f\"Author: {response}\")\n            self._last_author_response = response\n            self._test_results = f\"[Comment] Author: {response[:200]}\"\n            base_reward = 0.001\n\n        elif isinstance(action, AskQuestion):\n            self._comments.append(f\"Agent: {action.question}\")\n\n            response = self._author.respond(\n                agent_question=action.question,\n                test_results=self._test_results,\n                lint_results=self._lint_results,\n                doc_results=self._doc_results,\n                proposed_fix=None,\n                original_code=self._current_code                  # \u2190 FIXED\n            )\n\n            self._comments.append(f\"Author: {response}\")\n            self._last_author_response = response\n            self._test_results = f\"[Question] Author: {response[:200]}\"\n            base_reward = 0.002\n\n        # ==============================================================\n        # FINAL FIX ACTION\n        # ==============================================================\n        elif isinstance(action, ProposeFix):\n            if not action.fix_code:\n                base_reward = -0.05\n                self._done = True\n            else:\n                # Save original code BEFORE overwriting (for author.respond)\n                original_buggy = self._current_code\n                self._current_code = action.fix_code\n\n                runner = TestRunner(self._get_test_runner_bug_id())\n                test_score, test_output = runner.run_tests(self._current_code)\n                lint_score = self._run_linter_score(self._current_code)\n                negotiation_score = self._author.get_negotiation_score()\n\n                self._current_test_score = test_score\n                self._current_lint_score = lint_score\n\n                # Author gating \u2013 determines if the episode ends, reward is separate\n                threshold = self._author.thresholds.get(self._author.personality, 0.5)\n                if self._author._confidence < threshold:\n                    if self._step_count < self.max_steps:\n                        self._done = False\n                    else:\n                        self._done = True\n                else:\n                    self._done = True\n\n                # Get author's verbal feedback (pushback/acceptance)\n                author_feedback = self._author.respond(\n                    agent_comment=f\"Proposed fix:\\n{action.fix_code}\",\n                    test_results=f\"Score: {test_score:.2f}\",\n                    lint_results=f\"Score: {lint_score:.2f}\",\n                    doc_results=self._doc_results,\n                    proposed_fix=action.fix_code,\n                    original_code=original_buggy   # now correctly the buggy code, not the fix\n                )\n                self._test_results = f\"[Fix] Author: {author_feedback[:200]}\"\n                self._comments.append(f\"Author: {author_feedback}\")\n                self._last_author_response = author_feedback\n\n                base_reward = 0.001   # rubrics provide the real signal\n\n        # ==============================================================\n        # TERMINATION ACTIONS\n        # ==============================================================\n        elif isinstance(action, Skip):\n            base_reward = -0.03\n            self._done = True\n\n        elif isinstance(action, Done):\n            if self._tests_run:\n                base_reward = self._current_test_score * 0.5 - 0.2\n            else:\n                base_reward = -0.04\n            self._done = True\n\n        else:\n            base_reward = -0.02\n            self._done = True\n\n        # ==============================================================\n        # STEP UPDATE (before rubric computation so info contains final step)\n        # ==============================================================\n        self._step_count += 1\n        if self._step_count >= self.max_steps:\n            self._done = True\n\n        # Get fresh observation (needed for rubrics that may read obs)\n        obs = self._get_observation()\n\n        # Prepare info dict (rubrics may need action_type and deltas)\n        info = {\n            \"action_type\": action_type,\n            \"test_score\": self._current_test_score,\n            \"lint_score\": self._current_lint_score,\n            \"test_delta\": self._current_test_score - self._previous_test_score,\n            \"lint_delta\": self._current_lint_score - self._previous_lint_score,\n            \"prev_tests_run\": prev_tests_run,\n            \"prev_linter_run\": prev_linter_run,\n            \"prev_docs_queried\": prev_docs_queried,\n            \"docs_query_len\": len((action.query_topic or \"\").strip()) if isinstance(action, QueryDocs) else 0,\n            \"base_reward\": base_reward,\n        }\n\n        # ==============================================================\n        # COMPUTE FINAL REWARD USING RUBRICS\n        # ==============================================================\n        rubric_score = sum(r(self, action, obs, None, self._done, info) for r in self.rubrics)\n        final_reward = 0.4 * base_reward + rubric_score\n        final_reward = max(-1.0, min(1.0, final_reward))   # safety clip\n\n        # Track cumulative episode reward\n        self._episode_total_reward += final_reward\n\n        # Store episode total if done\n        if self._done:\n            self._episode_rewards.append(self._episode_total_reward)\n\n        # Complete info\n        info[\"final_reward\"] = final_reward\n        info[\"episode_total\"] = self._episode_total_reward\n\n        return obs, Reward(value=final_reward), self._done, info\n\n    # ===================================================================\n    def _run_linter_score(self, code: str) -> float:\n        \"\"\"Run pylint and return normalized score [0, 1].\"\"\"\n        try:\n            with tempfile.NamedTemporaryFile(mode='w', suffix='.py', delete=False) as f:\n                f.write(code)\n                tmp_path = f.name\n\n            result = subprocess.run(\n                ['pylint', tmp_path, '--score=y', '--exit-zero'],\n                capture_output=True,\n                text=True,\n                timeout=5\n            )\n\n            match = re.search(r\"rated at (\\d+\\.\\d+)/10\", result.stdout)\n            if match:\n                return float(match.group(1)) / 10.0\n            return 0.0\n        except:\n            return 0.0\n        finally:\n            try:\n                os.unlink(tmp_path)\n            except:\n                pass\n\n    # ===================================================================\n    def state(self) -> State:\n        \"\"\"Legacy compatibility.\"\"\"\n        return State(\n            pr_title=\"Code Review\",\n            pr_description=self._bug_description,\n            code_snippet=self._current_code,\n            comments=self._comments.copy(),\n            test_results=self._test_results,\n            step=self._step_count,\n            done=self._done\n        )\n\n# client.py \u2013 OpenEnv client entry point\n#from environment import CodeReviewEnv\n\n# The OpenEnv framework will import this class as the environment.\n__all__ = [\"CodeReviewEnv\"]\n\n# training.py  \u2013  PPO + QLoRA + Supervised Warm-up\n# Model : Qwen/Qwen2.5-1.5B-Instruct  (via Unsloth \u2013 2\u00d7 faster, fits Colab T4)\n# Fixed : label-masking, BPE-boundary alignment, log-ratio clamping, OOM guards\n# Evidence: reward curves, before/after traces, per-difficulty breakdown, KL, entropy\n# ============================================================\nimport os, json, random, re\nos.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\n\nimport torch\nimport torch.nn.functional as F\nfrom torch.optim import AdamW\nfrom dataclasses import dataclass, field\nfrom typing import List, Optional, Dict\nfrom collections import Counter, defaultdict\nimport numpy as np\n# \u2500\u2500 Unsloth gives 2\u00d7 throughput with identical outputs \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nfrom unsloth import FastLanguageModel\n\n\n# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n# CONFIG\n# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\nCFG = dict(\n    model_name       = \"unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit\",\n    max_seq_len      = 512,       # hard cap; prevents OOM on T4\n    lora_r           = 16,\n    lora_alpha       = 32,\n\n    # Warm-up\n    warmup_data      = \"training_data.json\",\n    warmup_epochs    = 2,\n    warmup_lr        = 2e-5,\n    warmup_grad_acc  = 4,         # effective batch = 4 examples\n\n    # PPO\n    ppo_iters        = 15,\n    trajs_per_iter   = 6,\n    max_steps        = 7,\n    ppo_lr           = 3e-5,\n    clip_eps         = 0.2,\n    entropy_coef     = 0.01,\n    gamma            = 0.99,\n    log_ratio_clamp  = 5.0,       # \u2190 prevents exp-explosion / NaN loss\n    temp_start       = 0.8,\n    temp_end         = 0.1,\n\n    # Eval\n    eval_episodes    = 10,        # episodes per evaluation snapshot\n)\n\nDEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\nTASK_LEVELS = list(BUG_DB.keys())   # [easy, medium, hard, harder, hardest]\n\n# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n# DATA STRUCTURES\n# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n@dataclass\nclass AgentAction:\n    action_type: str\n    content: Optional[str] = None\n\n@dataclass\nclass Trajectory:\n    states:   List[str]\n    actions:  List[str]\n    rewards:  List[float]\n    logprobs: List[float]\n    dones:    List[bool]\n    task:     str = \"\"\n\n@dataclass\nclass EvalSnapshot:\n    \"\"\"Captures full agent behaviour for before/after comparison.\"\"\"\n    avg_reward:    float\n    per_task:      Dict[str, float]  = field(default_factory=dict)\n    action_dist:   Dict[str, float]  = field(default_factory=dict)\n    success_rate:  float = 0.0\n    avg_steps:     float = 0.0\n    traces:        List[dict] = field(default_factory=list)\n\n# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n# ACTION PARSER\n# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\ndef parse_action(text: str) -> AgentAction:\n    \"\"\"Robust parser: tries strict JSON, then regex, then keyword heuristic.\"\"\"\n    text = text.strip()\n    try:\n        d = json.loads(text)\n        return AgentAction(d.get(\"action_type\",\"skip\").lower(), d.get(\"content\"))\n    except json.JSONDecodeError:\n        pass\n    m = re.search(r'\"action_type\"\\s*:\\s*\"(\\w+)\"', text)\n    if m:\n        cm = re.search(r'\"content\"\\s*:\\s*\"(.*?)\"', text, re.DOTALL)\n        return AgentAction(m.group(1).lower(), cm.group(1) if cm else None)\n    tl = text.lower()\n    for kw in (\"run_tests\",\"run_linter\",\"inspect\",\"query_docs\",\"fix\",\n               \"comment\",\"question\",\"done\"):\n        if kw in tl:\n            return AgentAction(kw)\n    return AgentAction(\"skip\")\n\ndef model_map_to_env(action: AgentAction):\n\n  \"\"\"Convert parsed agent output into environment action objects.\"\"\"\n  at = action.action_type\n  ct = action.content or \"\"\n\n  if at == \"run_tests\":\n    return RunTests()\n  elif at == \"run_linter\":\n      return RunLinter()\n  elif at == \"inspect\":\n      return Inspect()\n  elif at == \"query_docs\":\n      return QueryDocs(query_topic=ct)\n  elif at == \"fix\":\n      return ProposeFix(fix_code=ct)\n  elif at == \"comment\":\n      return WriteComment(comment_text=ct)\n  elif at == \"question\":\n      return AskQuestion(question=ct)\n  elif at == \"done\":\n      return Done()\n  else:\n      return Skip()\n  return model_map_to_env(action.action_type, action.content)\n\n# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n# MODEL  (Qwen2.5-1.5B via Unsloth)\n# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\ndef load_model():\n    print(f\"Loading {CFG['model_name']} \u2026\")\n    model, tokenizer = FastLanguageModel.from_pretrained(\n        model_name     = CFG[\"model_name\"],\n        max_seq_length = CFG[\"max_seq_len\"],\n        load_in_4bit   = True,\n    )\n    model = FastLanguageModel.get_peft_model(\n        model,\n        r              = CFG[\"lora_r\"],\n        lora_alpha     = CFG[\"lora_alpha\"],\n        target_modules = [\"q_proj\",\"k_proj\",\"v_proj\",\"o_proj\",\n                          \"gate_proj\",\"up_proj\",\"down_proj\"],\n        lora_dropout   = 0.0,\n        bias           = \"none\",\n        use_gradient_checkpointing = \"unsloth\",\n        random_state   = 3407,\n        max_seq_length = CFG[\"max_seq_len\"],\n        use_rslora     = False,\n        loftq_config   = None,\n    )\n    tokenizer.pad_token = tokenizer.eos_token\n    print(f\"  trainable params: \"\n          f\"{sum(p.numel() for p in model.parameters() if p.requires_grad)/1e6:.1f}M\")\n    return model, tokenizer\n\n# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n# PROMPT BUILDER\n# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\ndef build_prompt(obs, history_lines: List[str]) -> str:\n    author_msg   = getattr(obs, \"author_response\",  \"\") or \"\"\n    tool_output  = getattr(obs, \"last_tool_output\", \"\") or \"\"\n    personality  = getattr(obs, \"author_personality\",\"defensive\")\n\n    # Keep the diagnostics visible \u2013 don't truncate too early\n    if len(tool_output) > 800:\n        tool_output = tool_output[:800] + \" \u2026[truncated]\"\n\n    prompt = (\n        f\"You are a code review agent. Convince the developer to accept your fix.\\n\\n\"\n        f\"Developer personality: **{personality}** (needs evidence).\\n\"\n        f\"Your fix function MUST be named `fix`.\\n\\n\"\n        f\"Workflow:\\n\"\n        f\"1. `inspect`\\n\"\n        f\"2. `run_tests` and `run_linter`\\n\"\n        f\"3. `query_docs` if needed\\n\"\n        f\"4. **AFTER you have test + lint results, propose a fix (`fix`)**\\n\"\n        f\"5. Explain why it works (`comment`)\\n\"\n        f\"6. Once the developer agrees, `done`\\n\\n\"\n        f\"Code:\\n{obs.code_snippet}\\n\\n\"\n        f\"Author: {author_msg or '(no response yet \u2013 start with inspect)'}\\n\\n\"\n        f\"Last tool output:\\n{tool_output or '(none)'}\\n\\n\"\n        f\"Available actions: run_tests, run_linter, inspect, query_docs, fix, comment, question, done\\n\\n\"\n        f\"Respond ONLY in JSON: {{\\\"action_type\\\": \\\"...\\\", \\\"content\\\": \\\"...\\\"}}\\n\\n\"\n        f\"**IMPORTANT: Once you have test and lint results, you MUST propose a fix.**\"\n    )\n\n    if history_lines:\n        prompt += \"\\n\\nRecent steps:\\n\" + \"\\n\".join(history_lines[-4:])\n    return prompt\n\n# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n# BUG FIX 1 \u2013 label masking in supervised warmup\n# (original: labels=inputs[\"input_ids\"] trains on ALL tokens, including prompt)\n# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\ndef _masked_labels(input_ids: torch.Tensor, prompt_len: int) -> torch.Tensor:\n    \"\"\"Return labels with prompt positions set to -100 (ignored by CE loss).\"\"\"\n    labels = input_ids.clone()\n    labels[0, :prompt_len] = -100\n    return labels\n\n# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n# BUG FIX 2 \u2013 BPE-boundary-safe logprob computation\n# (original: tokenize(prompt) + tokenize(action) \u2260 tokenize(prompt+action))\n# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\ndef _compute_action_logprob(\n    logits:      torch.Tensor,   # [1, seq_len, vocab]\n    input_ids:   torch.Tensor,   # [1, seq_len]\n    prompt_len:  int,            # #tokens in the prompt part of the joint sequence\n) -> tuple:\n    \"\"\"\n    Compute sum of log-probs for *action* tokens only, using the jointly\n    tokenised sequence so BPE boundaries are respected.\n\n    Returns (total_logprob, avg_entropy, n_tokens).\n    \"\"\"\n    action_len = input_ids.shape[1] - prompt_len\n    if action_len <= 0:\n        return torch.tensor(0.0, device=DEVICE), torch.tensor(0.0, device=DEVICE), 0\n\n    total_lp  = torch.tensor(0.0, device=DEVICE)\n    total_ent = torch.tensor(0.0, device=DEVICE)\n\n    for k in range(action_len):\n        pos = prompt_len + k           # position of the k-th action token\n        pred_pos = pos - 1             # logit at pred_pos predicts token at pos\n        if pred_pos < 0 or pred_pos >= logits.shape[1]:\n            continue\n        token_id  = input_ids[0, pos]\n        lp_dist   = F.log_softmax(logits[0, pred_pos], dim=-1)\n        total_lp  = total_lp  + lp_dist[token_id]\n        probs     = torch.exp(lp_dist)\n        total_ent = total_ent + (-(probs * lp_dist).sum()).detach()\n\n    n = action_len\n    return total_lp, total_ent / max(n, 1), n\n\n# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n# GENERATION  (returns text + joint-sequence logprob)\n# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n@torch.no_grad()\ndef generate_action(prompt: str, model, tokenizer,\n                    temperature: float) -> tuple:\n    messages  = [{\"role\": \"user\", \"content\": prompt}]\n    formatted = tokenizer.apply_chat_template(\n        messages, tokenize=False, add_generation_prompt=True\n    )\n\n    inputs = tokenizer(\n        formatted, return_tensors=\"pt\",\n        max_length=CFG[\"max_seq_len\"],   # \u2190 fixed: no subtraction, use full length\n        truncation=True\n    ).to(DEVICE)\n    prompt_len = inputs[\"input_ids\"].shape[1]\n\n    gen_kwargs = dict(\n        max_new_tokens      = 128,\n        do_sample           = temperature > 0,\n        return_dict_in_generate = True,\n        output_scores       = True,\n        pad_token_id        = tokenizer.eos_token_id,\n        eos_token_id        = tokenizer.eos_token_id,\n    )\n    if temperature > 0:\n        gen_kwargs[\"temperature\"] = temperature\n\n    out     = model.generate(**inputs, **gen_kwargs)\n    gen_ids = out.sequences[0][prompt_len:]\n    text    = tokenizer.decode(gen_ids, skip_special_tokens=True).strip()\n\n    if not text:\n        fallback = random.choice([\n            '{\"action_type\":\"inspect\"}',\n            '{\"action_type\":\"run_tests\"}',\n            '{\"action_type\":\"run_linter\"}',\n        ])\n        print(f\"  [WARN] empty generation \u2192 fallback {fallback}\")\n        return fallback, -10.0\n\n    # Recompute logprob from the full joint sequence (BPE\u2011safe)\n    joint_ids = torch.cat(\n        [inputs[\"input_ids\"], gen_ids.unsqueeze(0).to(DEVICE)], dim=1\n    )\n    joint_ids = joint_ids[:, :CFG[\"max_seq_len\"]]\n\n    logits = model(input_ids=joint_ids).logits\n    lp, _, _ = _compute_action_logprob(logits, joint_ids, prompt_len)\n\n    return text, lp.item()\n\n# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n# TRAJECTORY COLLECTION\n# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\ndef collect_trajectory(env, model, tokenizer,\n                       max_steps: int, temperature: float,\n                       task: str) -> tuple:\n    env.set_task(task)\n    obs = env.reset()\n    history: List[str] = []\n    traj = Trajectory([], [], [], [], [], task=task)\n    action_seq = []\n\n    for _ in range(max_steps):\n        prompt = build_prompt(obs, history)\n        traj.states.append(prompt)\n\n        text, lp = generate_action(prompt, model, tokenizer, temperature)\n        traj.actions.append(text)\n        traj.logprobs.append(lp)\n\n        action = parse_action(text)\n        action_seq.append(action.action_type)\n\n        obs, reward, done, _ = env.step(model_map_to_env(action))\n        traj.rewards.append(float(np.clip(reward.value, -1.0, 1.0)))\n        traj.dones.append(done)\n\n        history.append(f\"Agent: {text[:120]}\")\n        history.append(f\"Env: {(obs.last_tool_output or '')[:120]}\")\n        if done:\n            break\n\n    return traj, action_seq\n\n# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n# SUPERVISED WARM-UP  (BUG FIX 1: action-only label masking)\n# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\ndef supervised_warmup(model, tokenizer):\n    print(\"\\n\" + \"=\"*60)\n    print(\"SUPERVISED WARM-UP\")\n    print(\"=\"*60)\n\n    with open(CFG[\"warmup_data\"], encoding=\"utf-8\") as f:\n        data = json.load(f)\n\n    opt = AdamW(model.parameters(), lr=CFG[\"warmup_lr\"])\n    model.train()\n    loss_history = []\n\n    for epoch in range(CFG[\"warmup_epochs\"]):\n        random.shuffle(data)\n        epoch_loss, n_valid = 0.0, 0\n        opt.zero_grad()\n\n        for step, ex in enumerate(data):\n            # \u2500\u2500 Tokenise prompt and full sequence jointly \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n            prompt_chat = tokenizer.apply_chat_template(\n                [{\"role\": \"user\", \"content\": ex[\"prompt\"]}],\n                tokenize=False, add_generation_prompt=True\n            )\n            full_chat = tokenizer.apply_chat_template(\n                [{\"role\": \"user\",      \"content\": ex[\"prompt\"]},\n                 {\"role\": \"assistant\", \"content\": ex[\"action\"]}],\n                tokenize=False\n            )\n\n            prompt_ids = tokenizer(\n                prompt_chat, return_tensors=\"pt\",\n                max_length=CFG[\"max_seq_len\"], truncation=True\n            )[\"input_ids\"]\n            full_inputs = tokenizer(\n                full_chat, return_tensors=\"pt\",\n                max_length=CFG[\"max_seq_len\"], truncation=True\n            ).to(DEVICE)\n\n            prompt_len = prompt_ids.shape[1]\n            if prompt_len >= full_inputs[\"input_ids\"].shape[1]:\n                continue  # action got truncated away\n\n            # BUG FIX 1 \u2500\u2500 mask prompt tokens so loss is action-only\n            labels = _masked_labels(full_inputs[\"input_ids\"], prompt_len)\n\n            out  = model(**full_inputs, labels=labels)\n            loss = out.loss / CFG[\"warmup_grad_acc\"]\n            loss.backward()\n\n            if (step + 1) % CFG[\"warmup_grad_acc\"] == 0:\n                torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n                opt.step()\n                opt.zero_grad()\n\n            epoch_loss += loss.item() * CFG[\"warmup_grad_acc\"]\n            n_valid    += 1\n\n            if (step + 1) % 50 == 0:\n                print(f\"  epoch {epoch+1}  step {step+1}/{len(data)}\"\n                      f\"  loss={epoch_loss/n_valid:.4f}\")\n\n        avg = epoch_loss / max(n_valid, 1)\n        loss_history.append(avg)\n        print(f\"  Epoch {epoch+1} complete: avg_loss={avg:.4f}\")\n\n    torch.cuda.empty_cache()\n    print(f\"\u2713 Warm-up done. Loss: {' \u2192 '.join(f'{l:.4f}' for l in loss_history)}\\n\")\n    return loss_history\n\n# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n# EVALUATION  (produces rich EvalSnapshot for comparison plots)\n# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n@torch.no_grad()\ndef evaluate(env, model, tokenizer, label: str = \"\") -> EvalSnapshot:\n    model.eval()\n    per_task: Dict[str, List[float]] = defaultdict(list)\n    action_counter: Counter = Counter()\n    all_steps, all_success = [], []\n    traces = []\n\n    for ep in range(CFG[\"eval_episodes\"]):\n        task = TASK_LEVELS[ep % len(TASK_LEVELS)]\n        traj, actions = collect_trajectory(\n          env, model, tokenizer, CFG[\"max_steps\"], 0.2, task\n        )\n\n        ep_r = sum(traj.rewards)\n        per_task[task].append(ep_r)\n        action_counter.update(actions)\n        all_steps.append(len(traj.actions))\n        all_success.append(1 if ep_r > 0 else 0)\n        traces.append({\"task\": task, \"reward\": round(ep_r, 4),\n                       \"steps\": len(traj.actions), \"actions\": actions})\n\n    total_actions = max(sum(action_counter.values()), 1)\n    snap = EvalSnapshot(\n        avg_reward   = float(np.mean([r for rs in per_task.values() for r in rs])),\n        per_task     = {t: float(np.mean(rs)) for t, rs in per_task.items()},\n        action_dist  = {a: c/total_actions for a, c in action_counter.most_common()},\n        success_rate = float(np.mean(all_success)),\n        avg_steps    = float(np.mean(all_steps)),\n        traces       = traces,\n    )\n    if label:\n        print(f\"\\n\u2500\u2500 {label} \u2500\u2500\")\n        print(f\"  avg_reward={snap.avg_reward:+.4f}  \"\n              f\"success={snap.success_rate:.0%}  steps={snap.avg_steps:.1f}\")\n        print(f\"  per-task: \" +\n              \"  \".join(f\"{t}={v:+.3f}\" for t,v in snap.per_task.items()))\n        print(f\"  top actions: \" +\n              \"  \".join(f\"{a}={p:.0%}\" for a,p in list(snap.action_dist.items())[:5]))\n    model.train()\n    return snap\n\n# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n# PPO UPDATE  (BUG FIX 2 + 3: BPE-safe logprob + log-ratio clamping)\n# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\ndef ppo_update(trajectories: List[Trajectory],\n               model, tokenizer, optimizer) -> dict:\n    model.train()\n    losses, kls, entropies = [], [], []\n\n    # \u2500\u2500 Compute discounted returns and a global mean baseline \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n    all_returns = []\n    traj_returns = []\n    for traj in trajectories:\n        ret, running = [], 0.0\n        for r, done in zip(reversed(traj.rewards), reversed(traj.dones)):\n            running = r + CFG[\"gamma\"] * (0.0 if done else running)\n            ret.insert(0, running)\n        traj_returns.append(ret)\n        all_returns.extend(ret)\n\n    baseline = float(np.mean(all_returns)) if all_returns else 0.0\n\n    for traj, returns in zip(trajectories, traj_returns):\n        for i in range(len(traj.states)):\n            state  = traj.states[i]\n            action = traj.actions[i]\n            old_lp = traj.logprobs[i]\n            adv    = returns[i] - baseline\n\n            # \u2500\u2500 Tokenise jointly (BPE FIX 2) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n            prompt_chat = tokenizer.apply_chat_template(\n                [{\"role\": \"user\", \"content\": state}],\n                tokenize=False, add_generation_prompt=True\n            )\n            full_text = prompt_chat + action\n\n            full_ids = tokenizer(\n                full_text, return_tensors=\"pt\",\n                max_length=CFG[\"max_seq_len\"], truncation=True\n            ).to(DEVICE)\n\n            # Count prompt tokens IN THE JOINT SEQUENCE (not separately)\n            prompt_ids = tokenizer(\n                prompt_chat, return_tensors=\"pt\",\n                max_length=CFG[\"max_seq_len\"] - 10, truncation=True\n            )[\"input_ids\"]\n            prompt_len = min(prompt_ids.shape[1], full_ids[\"input_ids\"].shape[1] - 1)\n\n            logits = model(**full_ids).logits\n\n            new_lp, avg_ent, n_tokens = _compute_action_logprob(\n                logits, full_ids[\"input_ids\"], prompt_len\n            )\n            if n_tokens == 0:\n                continue\n\n            # BUG FIX 3 \u2500\u2500 clamp log-ratio before exp to prevent NaN\n            old_lp_t  = torch.tensor(old_lp, dtype=torch.float32, device=DEVICE)\n            log_ratio = torch.clamp(new_lp - old_lp_t,\n                                    -CFG[\"log_ratio_clamp\"],\n                                     CFG[\"log_ratio_clamp\"])\n            ratio     = torch.exp(log_ratio)\n\n            adv_t = torch.tensor(adv, dtype=torch.float32, device=DEVICE)\n            s1    = ratio * adv_t\n            s2    = torch.clamp(ratio,\n                                1.0 - CFG[\"clip_eps\"],\n                                1.0 + CFG[\"clip_eps\"]) * adv_t\n\n            policy_loss = -torch.min(s1, s2)\n            loss        = policy_loss - CFG[\"entropy_coef\"] * avg_ent\n\n            if torch.isnan(loss) or torch.isinf(loss):\n                continue\n\n            optimizer.zero_grad()\n            loss.backward()\n            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n            optimizer.step()\n\n            losses.append(loss.item())\n            kls.append((old_lp_t - new_lp).detach().cpu().item())\n            entropies.append(avg_ent.item())\n\n    torch.cuda.empty_cache()\n    return dict(\n        loss    = float(np.mean(losses))    if losses    else 0.0,\n        kl      = float(np.mean(kls))       if kls       else 0.0,\n        entropy = float(np.mean(entropies)) if entropies else 0.0,\n    )\n\n# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n# PLOTTING  (rich evidence panel)\n# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\ndef plot_all(warmup_losses, reward_hist, success_hist, kl_hist, entropy_hist,\n             baseline_snap: EvalSnapshot,\n             postwarmup_snap: EvalSnapshot,\n             final_snap: EvalSnapshot):\n\n    iters = list(range(1, len(reward_hist) + 1))\n\n    # \u2500\u2500 Figure 1: training curves (2\u00d73 grid) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n    fig = plt.figure(figsize=(18, 10))\n    gs  = gridspec.GridSpec(2, 3, figure=fig, hspace=0.45, wspace=0.35)\n\n    # (0,0) Warm-up loss\n    ax = fig.add_subplot(gs[0, 0])\n    ax.plot(range(1, len(warmup_losses)+1), warmup_losses,\n            marker=\"o\", color=\"mediumpurple\", linewidth=2)\n    ax.set_title(\"A. Warm-up CE Loss \u2193\", fontweight=\"bold\")\n    ax.set_xlabel(\"Epoch\"); ax.set_ylabel(\"Loss\"); ax.grid(alpha=0.3)\n\n    # (0,1) PPO reward\n    ax = fig.add_subplot(gs[0, 1])\n    smooth = np.convolve(reward_hist, np.ones(3)/3, mode=\"same\")\n    ax.plot(iters, reward_hist, alpha=0.35, color=\"steelblue\", linewidth=1)\n    ax.plot(iters, smooth, color=\"steelblue\", linewidth=2.5, label=\"reward (smoothed)\")\n    ax.axhline(baseline_snap.avg_reward, color=\"gray\", linestyle=\":\",\n               label=f\"pre-warmup ({baseline_snap.avg_reward:+.3f})\")\n    ax.axhline(postwarmup_snap.avg_reward, color=\"mediumpurple\", linestyle=\"--\",\n               label=f\"post-warmup ({postwarmup_snap.avg_reward:+.3f})\")\n    ax.axhline(final_snap.avg_reward, color=\"forestgreen\", linestyle=\"-.\",\n               label=f\"final ({final_snap.avg_reward:+.3f})\")\n    ax.set_title(\"B. PPO Reward \u2191\", fontweight=\"bold\")\n    ax.set_xlabel(\"Iteration\"); ax.set_ylabel(\"Avg Reward\")\n    ax.legend(fontsize=7); ax.grid(alpha=0.3)\n\n    # (0,2) Success rate\n    ax = fig.add_subplot(gs[0, 2])\n    ax.plot(iters, success_hist, marker=\"s\", color=\"seagreen\", linewidth=2)\n    ax.set_ylim(0, 1)\n    ax.set_title(\"C. Episode Success Rate \u2191\", fontweight=\"bold\")\n    ax.set_xlabel(\"Iteration\"); ax.set_ylabel(\"Fraction\")\n    ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda y,_: f\"{y:.0%}\"))\n    ax.grid(alpha=0.3)\n\n    # (1,0) KL divergence\n    ax = fig.add_subplot(gs[1, 0])\n    ax.plot(iters, kl_hist, marker=\"^\", color=\"tomato\", linewidth=2)\n    ax.axhline(0, color=\"gray\", linewidth=0.8)\n    ax.set_title(\"D. KL Divergence\", fontweight=\"bold\")\n    ax.set_xlabel(\"Iteration\"); ax.set_ylabel(\"KL\"); ax.grid(alpha=0.3)\n\n    # (1,1) Entropy\n    ax = fig.add_subplot(gs[1, 1])\n    ax.plot(iters, entropy_hist, marker=\"D\", color=\"darkorange\", linewidth=2)\n    ax.set_title(\"E. Policy Entropy\", fontweight=\"bold\")\n    ax.set_xlabel(\"Iteration\"); ax.set_ylabel(\"Entropy\"); ax.grid(alpha=0.3)\n\n    # (1,2) Per-difficulty final reward\n    ax = fig.add_subplot(gs[1, 2])\n    tasks = TASK_LEVELS\n    vals_base  = [baseline_snap.per_task.get(t, 0)   for t in tasks]\n    vals_final = [final_snap.per_task.get(t, 0)       for t in tasks]\n    x = np.arange(len(tasks))\n    ax.bar(x - 0.2, vals_base,  0.35, label=\"baseline\",color=\"lightcoral\",  alpha=0.8)\n    ax.bar(x + 0.2, vals_final, 0.35, label=\"final\",   color=\"steelblue\",   alpha=0.8)\n    ax.set_xticks(x); ax.set_xticklabels(tasks, fontsize=8)\n    ax.set_title(\"F. Per-Difficulty Reward\", fontweight=\"bold\")\n    ax.set_ylabel(\"Avg Reward\"); ax.legend(fontsize=8); ax.grid(alpha=0.3, axis=\"y\")\n    ax.axhline(0, color=\"gray\", linewidth=0.8)\n\n    fig.suptitle(f\"Code-Review Agent \u2013 Full Training Evidence  \"\n                 f\"(Qwen2.5-1.5B, PPO + QLoRA)\",\n                 fontsize=13, fontweight=\"bold\")\n    fig.savefig(\"training_summary.png\", dpi=150, bbox_inches=\"tight\")\n    plt.close(fig)\n    print(\"  Saved: training_summary.png\")\n\n    # \u2500\u2500 Figure 2: before / after action distribution \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n    fig, axes = plt.subplots(1, 3, figsize=(16, 4), sharey=False)\n    for ax, snap, title in zip(\n        axes,\n        [baseline_snap, postwarmup_snap, final_snap],\n        [\"Before (baseline)\", \"After warm-up\", \"After PPO (final)\"]\n    ):\n        if snap.action_dist:\n            labels = list(snap.action_dist.keys())\n            vals   = [snap.action_dist[l]*100 for l in labels]\n            bars   = ax.barh(labels, vals,\n                             color=plt.cm.tab10(np.linspace(0, 0.8, len(labels))))\n            ax.bar_label(bars, fmt=\"%.0f%%\", padding=3, fontsize=8)\n        ax.set_xlim(0, 105)\n        ax.set_title(title, fontweight=\"bold\")\n        ax.set_xlabel(\"% of actions\")\n        ax.grid(alpha=0.3, axis=\"x\")\n\n    fig.suptitle(\"Action Distribution: Before vs After Training\",\n                 fontsize=12, fontweight=\"bold\")\n    plt.tight_layout()\n    fig.savefig(\"action_distribution.png\", dpi=150, bbox_inches=\"tight\")\n    plt.close(fig)\n    print(\"  Saved: action_distribution.png\")\n\n# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n# MAIN\n# \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\ndef train():\n    model, tokenizer = load_model()\n    env = CodeReviewEnv()\n\n    # \u2500\u2500 PHASE 0: pre-warmup baseline \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n    print(\"\\n\" + \"=\"*60)\n    print(\"PHASE 0 \u2013 BASELINE (untrained)\")\n    print(\"=\"*60)\n    baseline_snap = evaluate(env, model, tokenizer, \"Baseline\")\n\n    # \u2500\u2500 PHASE 1: supervised warm-up \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n    warmup_losses = supervised_warmup(model, tokenizer)\n\n    postwarmup_snap = evaluate(env, model, tokenizer, \"Post-Warmup\")\n\n    # \u2500\u2500 PHASE 2: PPO \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n    optimizer = AdamW(model.parameters(), lr=CFG[\"ppo_lr\"])\n    reward_hist, success_hist, kl_hist, entropy_hist = [], [], [], []\n\n    print(\"\\n\" + \"=\"*60)\n    print(f\"PHASE 2 \u2013 PPO ({CFG['ppo_iters']} iterations \u00d7 \"\n          f\"{CFG['trajs_per_iter']} trajectories)\")\n    print(\"=\"*60)\n\n    for it in range(CFG[\"ppo_iters\"]):\n        # Linearly anneal exploration temperature\n        t = CFG[\"temp_start\"] + (CFG[\"temp_end\"] - CFG[\"temp_start\"]) * (it / max(CFG[\"ppo_iters\"]-1, 1))\n\n        print(f\"\\n\u2500\u2500 Iteration {it+1}/{CFG['ppo_iters']}  temp={t:.2f} \u2500\u2500\")\n        trajectories, action_counts = [], Counter()\n        successes = 0\n\n        for j in range(CFG[\"trajs_per_iter\"]):\n            task = TASK_LEVELS[j % len(TASK_LEVELS)]\n            traj, actions = collect_trajectory(\n                env, model, tokenizer, CFG[\"max_steps\"], t, task\n            )\n            trajectories.append(traj)\n            action_counts.update(actions)\n            ep_r = sum(traj.rewards)\n            successes += int(ep_r > 0)\n            print(f\"  traj {j+1}/{CFG['trajs_per_iter']}  task={task}\"\n                  f\"  steps={len(traj.actions)}  reward={ep_r:+.3f}\")\n\n        avg_r       = float(np.mean([sum(t.rewards) for t in trajectories]))\n        success_r   = successes / CFG[\"trajs_per_iter\"]\n\n        m = ppo_update(trajectories, model, tokenizer, optimizer)\n\n        reward_hist.append(avg_r)\n        success_hist.append(success_r)\n        kl_hist.append(m[\"kl\"])\n        entropy_hist.append(m[\"entropy\"])\n\n        delta = avg_r - baseline_snap.avg_reward\n        print(f\"  \u2192 avg_reward={avg_r:+.4f}  \u0394baseline={delta:+.4f}\"\n              f\"  success={success_r:.0%}\"\n              f\"  loss={m['loss']:.4f}  kl={m['kl']:.4f}  ent={m['entropy']:.4f}\")\n        print(f\"  actions: {dict(action_counts.most_common(5))}\")\n\n    # \u2500\u2500 PHASE 3: final evaluation \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n    print(\"\\n\" + \"=\"*60)\n    print(\"PHASE 3 \u2013 FINAL EVALUATION\")\n    print(\"=\"*60)\n    final_snap = evaluate(env, model, tokenizer, \"Final\")\n\n    # \u2500\u2500 Summary table \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n    print(\"\\n\" + \"=\"*60)\n    print(\"TRAINING SUMMARY\")\n    print(\"=\"*60)\n    print(f\"  {'Stage':<20} {'Reward':>10} {'Success':>10} {'\u0394 baseline':>12}\")\n    print(f\"  {'-'*54}\")\n    for label, snap in [(\"Baseline\",    baseline_snap),\n                        (\"Post-warmup\", postwarmup_snap),\n                        (\"Final (PPO)\", final_snap)]:\n        delta = snap.avg_reward - baseline_snap.avg_reward\n        print(f\"  {label:<20} {snap.avg_reward:>+10.4f}\"\n              f\" {snap.success_rate:>10.0%}  {delta:>+11.4f}\")\n\n    improve = final_snap.avg_reward - baseline_snap.avg_reward\n    verdict = \"\u2713 LEARNED\" if improve > 0 else \"\u2717 NO IMPROVEMENT\"\n    print(f\"\\n  {verdict}  (total \u0394 = {improve:+.4f})\")\n\n    print(\"\\nBefore \u2192 After traces (one per difficulty):\")\n    btask = {t[\"task\"]: t for t in baseline_snap.traces}\n    ftask = {t[\"task\"]: t for t in final_snap.traces}\n    for task in TASK_LEVELS:\n        b = btask.get(task, {})\n        f = ftask.get(task, {})\n        print(f\"  {task:8s}  baseline actions={b.get('actions',[])}  \"\n              f\"reward={b.get('reward',0):+.3f}\"\n              f\"  \u2502  final actions={f.get('actions',[])}  \"\n              f\"reward={f.get('reward',0):+.3f}\")\n\n    # \u2500\u2500 Plots \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n    plot_all(warmup_losses, reward_hist, success_hist, kl_hist, entropy_hist,\n             baseline_snap, postwarmup_snap, final_snap)\n\n    print(\"\\nAll done. Saved: training_summary.png  action_distribution.png\")\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": "CFG.update({\n    \"warmup_epochs\": 1,\n    \"ppo_iters\": 150,\n    \"trajs_per_iter\": 4,\n    \"max_steps\": 6,\n    \"eval_episodes\": 5,\n})\n\nprint(\"Colab T4 defaults loaded:\")\nfor key in (\"model_name\", \"max_seq_len\", \"warmup_epochs\", \"ppo_iters\", \"trajs_per_iter\", \"max_steps\", \"eval_episodes\"):\n    print(f\"  {key}: {CFG[key]}\")\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": "# Auto-injected: capture training metrics for separate plot cells\n_TRAIN_STORE = {}\n_orig_plot_all = plot_all\n\ndef plot_all_patched(warmup_losses, reward_hist, success_hist, kl_hist,\n                     entropy_hist, baseline_snap, postwarmup_snap, final_snap):\n    _orig_plot_all(warmup_losses, reward_hist, success_hist, kl_hist,\n                   entropy_hist, baseline_snap, postwarmup_snap, final_snap)\n    _TRAIN_STORE.update({\n        'warmup_losses': list(warmup_losses),\n        'reward_hist':   list(reward_hist),\n        'success_hist':  list(success_hist),\n        'kl_hist':       list(kl_hist),\n        'entropy_hist':  list(entropy_hist),\n        'baseline':      baseline_snap,\n        'postwarmup':    postwarmup_snap,\n        'final':         final_snap,\n    })\n\nplot_all = plot_all_patched\nprint(\"Training metric capture ready.\")\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": "train()"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": "from IPython.display import Image, display\nimport os\n\nfor path in (\"training_summary.png\", \"action_distribution.png\"):\n    if os.path.exists(path):\n        display(Image(filename=path))\n    else:\n        print(f\"Missing artifact: {path}\")\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": "# \u2500\u2500 Plot 1: PPO Reward Curve \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nif _TRAIN_STORE:\n    rh     = _TRAIN_STORE['reward_hist']\n    iters  = list(range(1, len(rh)+1))\n    smooth = np.convolve(rh, np.ones(min(5, len(rh)))/min(5, len(rh)), mode='same')\n\n    fig, ax = plt.subplots(figsize=(11, 4))\n    ax.plot(iters, rh, alpha=0.3, color='steelblue', linewidth=1, label='Raw reward')\n    ax.plot(iters, smooth, color='steelblue', linewidth=2.5, label='Smoothed (window=5)')\n    ax.axhline(_TRAIN_STORE['baseline'].avg_reward,\n               color='gray', linestyle=':', linewidth=1.5,\n               label=f\"Baseline ({_TRAIN_STORE['baseline'].avg_reward:+.3f})\")\n    ax.axhline(_TRAIN_STORE['postwarmup'].avg_reward,\n               color='mediumpurple', linestyle='--', linewidth=1.5,\n               label=f\"Post-warmup ({_TRAIN_STORE['postwarmup'].avg_reward:+.3f})\")\n    ax.axhline(_TRAIN_STORE['final'].avg_reward,\n               color='forestgreen', linestyle='-.', linewidth=1.5,\n               label=f\"Final ({_TRAIN_STORE['final'].avg_reward:+.3f})\")\n    ax.set_title('PPO Reward Curve (150 iterations)', fontsize=14, fontweight='bold')\n    ax.set_xlabel('PPO Iteration')\n    ax.set_ylabel('Avg Episode Reward')\n    ax.legend(fontsize=9)\n    ax.grid(alpha=0.3)\n    plt.tight_layout()\n    plt.savefig('reward_curve.png', dpi=150, bbox_inches='tight')\n    plt.show()\n    print('Saved: reward_curve.png')\nelse:\n    print('Run train() first.')\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": "# \u2500\u2500 Plot 2: Comparison Curve \u2013 Baseline vs Post-Warmup vs Final \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nif _TRAIN_STORE:\n    tasks   = list(_TRAIN_STORE['final'].per_task.keys())\n    vals_b  = [_TRAIN_STORE['baseline'].per_task.get(t, 0)   for t in tasks]\n    vals_pw = [_TRAIN_STORE['postwarmup'].per_task.get(t, 0) for t in tasks]\n    vals_f  = [_TRAIN_STORE['final'].per_task.get(t, 0)      for t in tasks]\n\n    x, w = np.arange(len(tasks)), 0.25\n    fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n\n    # Left: per-difficulty bars\n    ax = axes[0]\n    ax.bar(x - w, vals_b,  w, label='Baseline',    color='lightcoral',   alpha=0.85)\n    ax.bar(x,     vals_pw, w, label='Post-warmup', color='mediumpurple', alpha=0.85)\n    ax.bar(x + w, vals_f,  w, label='Final (PPO)', color='steelblue',    alpha=0.85)\n    ax.axhline(0, color='gray', linewidth=0.8)\n    ax.set_xticks(x); ax.set_xticklabels(tasks, fontsize=9)\n    ax.set_title('Per-Difficulty Reward: 3-Stage Comparison', fontweight='bold')\n    ax.set_ylabel('Avg Episode Reward'); ax.legend(); ax.grid(alpha=0.3, axis='y')\n\n    # Right: success rate trajectory\n    ax = axes[1]\n    sh = _TRAIN_STORE['success_hist']\n    iters_s = range(1, len(sh)+1)\n    ax.plot(iters_s, sh, color='seagreen', linewidth=2.5, marker='s', markersize=3)\n    ax.fill_between(iters_s, sh, alpha=0.15, color='seagreen')\n    ax.set_ylim(0, 1.05)\n    ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda y, _: f'{y:.0%}'))\n    ax.set_title('Success Rate Over 150 PPO Iterations', fontweight='bold')\n    ax.set_xlabel('PPO Iteration'); ax.set_ylabel('Fraction Successful')\n    ax.grid(alpha=0.3)\n\n    fig.suptitle('Comparison: Baseline \u2192 Post-Warmup \u2192 Final (PPO)', fontsize=13, fontweight='bold')\n    plt.tight_layout()\n    plt.savefig('comparison_curve.png', dpi=150, bbox_inches='tight')\n    plt.show()\n    print('Saved: comparison_curve.png')\nelse:\n    print('Run train() first.')\n"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": "# \u2500\u2500 Plot 3: Loss Graph \u2013 Warmup CE Loss, KL Divergence, Policy Entropy \u2500\u2500\u2500\u2500\u2500\u2500\u2500\nif _TRAIN_STORE:\n    fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n\n    # Warmup CE loss\n    wl = _TRAIN_STORE['warmup_losses']\n    axes[0].plot(range(1, len(wl)+1), wl, marker='o', color='mediumpurple', linewidth=2.5)\n    axes[0].set_title('Supervised Warm-up Loss \u2193', fontweight='bold')\n    axes[0].set_xlabel('Epoch'); axes[0].set_ylabel('Cross-Entropy Loss')\n    axes[0].grid(alpha=0.3)\n\n    # KL divergence\n    kh = _TRAIN_STORE['kl_hist']\n    axes[1].plot(range(1, len(kh)+1), kh, marker='^', color='tomato', linewidth=2)\n    axes[1].axhline(0, color='gray', linewidth=0.8, linestyle='--')\n    axes[1].set_title('PPO KL Divergence', fontweight='bold')\n    axes[1].set_xlabel('Iteration'); axes[1].set_ylabel('KL')\n    axes[1].grid(alpha=0.3)\n\n    # Policy entropy\n    eh = _TRAIN_STORE['entropy_hist']\n    axes[2].plot(range(1, len(eh)+1), eh, marker='D', color='darkorange', linewidth=2)\n    axes[2].set_title('Policy Entropy', fontweight='bold')\n    axes[2].set_xlabel('Iteration'); axes[2].set_ylabel('Entropy')\n    axes[2].grid(alpha=0.3)\n\n    fig.suptitle('Loss / Divergence Metrics Across Training', fontsize=13, fontweight='bold')\n    plt.tight_layout()\n    plt.savefig('loss_graph.png', dpi=150, bbox_inches='tight')\n    plt.show()\n    print('Saved: loss_graph.png')\nelse:\n    print('Run train() first.')\n"
  }
 ]
}