spec_version: 1
name: codereview-env
type: space
runtime: fastapi
app: server.app:app
port: 7860

description: >
  A real-world OpenEnv RL environment for training LLM agents to write
  high-quality, actionable code reviews. Agents learn to inspect PR diffs,
  request more context, and submit structured findings across three
  difficulty tiers (easy → medium → hard).

author: "Anurag137"
tags:
  - code-review
  - software-engineering
  - reinforcement-learning
  - llm-evaluation
  - openenv
  - security
  - hackathon-2026

# ── Task registry ──────────────────────────────────────────────────────────────
tasks:
  - id: pagination-regression
    difficulty: easy
    title: "Review a pagination bug fix before release"
    description: >
      A pagination helper was edited after customer reports that page 2 skips results.
      Identify whether the fix is correct AND whether any remaining production risk exists
      (page 0 / negative pages create dangerous negative-index slices).
    step_limit: 4
    reward_range: [0.01, 0.99]
    grader: server.tasks.grade_findings

  - id: tenant-export-auth
    difficulty: medium
    title: "Review a multi-tenant export endpoint"
    description: >
      A finance CSV-export endpoint was added for admins in a multi-tenant SaaS system.
      The route reads account_id from query params without scope validation — a cross-tenant
      data leak and missing admin-role gate must be caught.
    step_limit: 5
    reward_range: [0.01, 0.99]
    grader: server.tasks.grade_findings

  - id: refund-idempotency
    difficulty: hard
    title: "Review a refund worker retry patch"
    description: >
      A refund worker was patched after a duplicate-refund incident. The patch retries
      without an idempotency key, and concurrent workers can both send the refund due to
      a status-update race. A missing regression test leaves the risk uncovered.
    step_limit: 6
    reward_range: [0.01, 0.99]
    grader: server.tasks.grade_findings

# ── Episode parameters ─────────────────────────────────────────────────────────
episode:
  type: multi-step
  max_steps: 7
  action_types:
    - open_artifact   # Explore additional context (partial reward)
    - submit_review   # Submit findings (graded; ends episode)

# ── Reward function ────────────────────────────────────────────────────────────
reward:
  range: [0.01, 0.99]
  layers:
    - name: exploration_bonus
      description: "+0.05 per unique artifact opened before submitting"
      max: 0.35
    - name: task_grader
      description: "Deterministic keyword + quality grader (70% weight)"
      max: 0.70
    - name: llm_reward
      description: "Two-layer LLM judge — programmatic (0.40) + semantic (0.60), 30% weight"
      max: 0.30