auto-review-loop

# Auto Review Loop: Autonomous Research Improvement Autonomously iterate: review → implement fixes → re-review, until the external reviewer gives a positive assessment or MAX_ROUNDS is reached. ## Context: $ARGUMENTS ## Constants - MAX_ROUNDS = 4 - POSITIVE_THRESHOLD: score >= 6/10, or verdict contains "accept", "sufficient", "ready for submission" - REVIEW_DOC: `AUTO_REVIEW.md` in project root (cumulative log) - REVIEWER_MODEL = `gpt-5.4` — Model used via a secondary Codex agent. Must be an OpenAI model (e.g., `gpt-5.4`, `o3`, `gpt-4o`) - **HUMAN_CHECKPOINT = false** — When `true`, pause after each round's review (Phase B) and present the score + weaknesses to the user. Wait for user input before proceeding to Phase C. The user can: approve the suggested fixes, provide custom modification instructions, skip specific fixes, or stop the loop early. When `false` (default), the loop runs fully autonomously. - **COMPACT = false** — When `true`, (1) read `EXPERIMENT_LOG.md` and `findings.md` instead of parsing full logs on session recovery, (2) append key findings to `findings.md` after each round. > 💡 Override: `/auto-review-loop "topic" — compact: true, human checkpoint: true` ## State Persistence (Compact Recovery) Long-running loops may hit the context window limit, triggering automatic compaction. To survive this, persist state to `REVIEW_STATE.json` after each round: ```json { "round": 2, "agent_id": "019cd392-...", "status": "in_progress", "last_score": 5.0...