iiinit skills agents and model

.cursor/plans/skill_agent_pptx_5413e3c2.plan.md

@@ -4,7 +4,7 @@ overview: Add a Hermes-style skill agent library on top of your existing Transfo
 todos:
   - id: agent-lib
     content: "Create libs/agent: SkillRegistry, ToolRegistry, AgentRunner, TraceRecorder, pydantic outline models"
-    status: pending
+    status: in_progress
   - id: pptx-skill
     content: Add skills/education-pptx/SKILL.md and create_pptx tool (python-pptx)
     status: pending

libs/agent/README.md

@@ -0,0 +1,17 @@
+# agent
+
+Hermes-style skill agent on top of the local `inference` backends.
+
+```python
+from agent.runner import AgentRunner
+from inference.factory import get_backend
+
+runner = AgentRunner()
+result = runner.run_education_pptx(
+    topic="Photosynthesis",
+    grade="6",
+    slide_count=5,
+    model_key="minicpm5-1b",
+    backend=get_backend("minicpm5-1b"),
+)
+```

libs/agent/pyproject.toml

@@ -0,0 +1,22 @@
+[project]
+name = "agent"
+version = "0.1.0"
+description = "Skill-based local agent loop for hackathon tasks"
+readme = "README.md"
+authors = [
+    { name = "MSGhais", email = "msghais135@gmail.com" }
+]
+requires-python = ">=3.12"
+dependencies = [
+    "inference",
+    "pydantic>=2.0.0",
+    "python-pptx>=1.0.0",
+    "pyyaml>=6.0.2",
+]
+
+[tool.uv.sources]
+inference = { workspace = true }
+
+[build-system]
+requires = ["uv_build>=0.8.13,<0.9.0"]
+build-backend = "uv_build"

libs/agent/src/agent/__init__.py

@@ -0,0 +1,11 @@
+from agent.runner import AgentResult, AgentRunner
+from agent.skills import Skill, SkillRegistry
+from agent.trace import TraceRecorder
+
+__all__ = [
+    "AgentResult",
+    "AgentRunner",
+    "Skill",
+    "SkillRegistry",
+    "TraceRecorder",
+]

libs/agent/src/agent/models.py

@@ -0,0 +1,20 @@
+from __future__ import annotations
+
+from pydantic import BaseModel, Field
+
+
+class SlideSpec(BaseModel):
+    title: str
+    bullets: list[str] = Field(default_factory=list, min_length=1)
+    speaker_note: str = ""
+
+
+class SlideOutline(BaseModel):
+    title: str
+    slides: list[SlideSpec] = Field(min_length=1)
+
+
+class EducationPptxInput(BaseModel):
+    topic: str
+    grade: str
+    slide_count: int = Field(ge=3, le=8)

libs/agent/src/agent/trace.py

@@ -0,0 +1,78 @@
+from __future__ import annotations
+
+import hashlib
+import json
+import uuid
+from dataclasses import dataclass, field
+from datetime import UTC, datetime
+from pathlib import Path
+from typing import Any
+
+
+def _prompt_hash(text: str) -> str:
+    return hashlib.sha256(text.encode()).hexdigest()[:16]
+
+
+@dataclass
+class TraceRecorder:
+    skill: str
+    model: str
+    user_input: dict[str, Any]
+    run_id: str = field(default_factory=lambda: uuid.uuid4().hex[:12])
+    steps: list[dict[str, Any]] = field(default_factory=list)
+    artifact: str | None = None
+    created_at: str = field(default_factory=lambda: datetime.now(UTC).isoformat())
+
+    def log_llm(self, prompt: str, output: str) -> None:
+        self.steps.append(
+            {
+                "type": "llm",
+                "prompt_hash": _prompt_hash(prompt),
+                "output": output,
+            }
+        )
+
+    def log_tool(self, name: str, arguments: dict[str, Any], result: str) -> None:
+        self.steps.append(
+            {
+                "type": "tool",
+                "name": name,
+                "arguments": arguments,
+                "result": result,
+            }
+        )
+
+    def set_artifact(self, path: str) -> None:
+        self.artifact = path
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "run_id": self.run_id,
+            "skill": self.skill,
+            "model": self.model,
+            "input": self.user_input,
+            "steps": self.steps,
+            "artifact": self.artifact,
+            "created_at": self.created_at,
+        }
+
+    def to_json(self, *, indent: int = 2) -> str:
+        return json.dumps(self.to_dict(), indent=indent)
+
+    def save(self, traces_dir: Path | None = None) -> Path:
+        base = traces_dir or _default_traces_dir()
+        base.mkdir(parents=True, exist_ok=True)
+        path = base / f"{self.run_id}.json"
+        path.write_text(self.to_json())
+        return path
+
+
+def _default_traces_dir() -> Path:
+    env = __import__("os").environ.get("AGENT_TRACES_DIR")
+    if env:
+        return Path(env)
+    for base in (Path.cwd(), *Path.cwd().parents):
+        candidate = base / "outputs" / "traces"
+        if (base / "models.yaml").is_file() or (base / "pyproject.toml").is_file():
+            return candidate
+    return Path.cwd() / "outputs" / "traces"