auto-review-loop

分类: 开发与编程 | 上传者: wanshuiyin | 下载: 0 | 版本: v1.0（最新）

Autonomous multi-round research review loop. Repeatedly reviews via Codex MCP, implements fixes, and re-reviews until positive assessment or max rounds reached. Use when user says "auto review loop", "review until it passes", or wants autonomous iterative improvement.

更新日志: Source: GitHub https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep

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SKILL.md

---
name: auto-review-loop
description: Autonomous multi-round research review loop. Repeatedly reviews via Codex MCP, implements fixes, and re-reviews until positive assessment or max rounds reached. Use when user says "auto review loop", "review until it passes", or wants autonomous iterative improvement.
argument-hint: [topic-or-scope]
allowed-tools: Bash(*), Read, Grep, Glob, Write, Edit, Agent, Skill, mcp__codex__codex, mcp__codex__codex-reply
---

# 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)

## Workflow

### Initialization

1. Read project narrative documents, memory files, and any prior review documents
2. Read recent experiment results (check output directories, logs)
3. Identify current weaknesses and open TODOs from prior reviews
4. Initialize round counter = 1
5. Create/update `AUTO_REVIEW.md` with header and timestamp

### Loop (repeat up to MAX_ROUNDS)

#### Phase A: Review

Send comprehensive context to the external reviewer:

```
mcp__codex__codex:
  config: {"model_reasoning_effort": "xhigh"}
  prompt: |
    [Round N/MAX_ROUNDS of autonomous review loop]

    [Full research context: claims, methods, results, known weaknesses]
    [Changes since last round, if any]

    Please act as a senior ML reviewer (NeurIPS/ICML level).

    1. Score this work 1-10 for a top venue
    2. List remaining critical weaknesses (ranked by severity)
    3. For each weakness, specify the MINIMUM fix (experiment, analysis, or reframing)
    4. State clearly: is this READY for submission? Yes/No/Almost

    Be brutally honest. If the work is ready, say so clearly.
```

If this is round 2+, use `mcp__codex__codex-reply` with the saved threadId to maintain conversation context.

#### Phase B: Parse Assessment

Extract from the response:
- **Score** (numeric 1-10)
- **Verdict** ("ready" / "almost" / "not ready")
- **Action items** (ranked list of fixes)

**STOP CONDITION**: If score >= 6 AND verdict contains "ready" or "almost" → stop loop, document final state.

#### Phase C: Implement Fixes (if not stopping)

For each action item (highest priority first):

1. **Code changes**: Write/modify experiment scripts, model code, analysis scripts
2. **Run experiments**: Deploy to GPU server via SSH + screen/tmux
3. **Analysis**: Run evaluation, collect results, update figures/tables
4. **Documentation**: Update project notes and review document

Prioritization rules:
- Skip fixes requiring excessive compute (flag for manual follow-up)
- Skip fixes requiring external data/models not available
- Prefer reframing/analysis over new experiments when both address the concern
- Always implement metric additions (cheap, high impact)

#### Phase D: Wait for Results

If experiments were launched:
- Monitor remote sessions for completion
- Collect results from output files and logs

#### Phase E: Document Round

Append to `AUTO_REVIEW.md`:

```markdown
## Round N (timestamp)

### Assessment
- Score: X/10
- Verdict: [ready/almost/not ready]
- Key criticisms: [bullet list]

### Actions Taken
- [what was implemented/changed]

### Results
- [experiment outcomes, if any]

### Status
- [continuing to round N+1 / stopping]
```

Increment round counter → back to Phase A.

### Termination

When loop ends (positive assessment or max rounds):

1. Write final summary to `AUTO_REVIEW.md`
2. Update project notes with conclusions
3. If stopped at max rounds without positive assessment:
   - List remaining blockers
   - Estimate effort needed for each
   - Suggest whether to continue manually or pivot

## Key Rules

- ALWAYS use `config: {"model_reasoning_effort": "xhigh"}` for maximum reasoning depth
- Save threadId from first call, use `mcp__codex__codex-reply` for subsequent rounds
- Be honest — include negative results and failed experiments
- Do NOT hide weaknesses to game a positive score
- Implement fixes BEFORE re-reviewing (don't just promise to fix)
- If an experiment takes > 30 minutes, launch it and continue with other fixes while waiting
- Document EVERYTHING — the review log should be self-contained
- Update project notes after each round, not just at the end

## Prompt Template for Round 2+

```
mcp__codex__codex-reply:
  threadId: [saved from round 1]
  config: {"model_reasoning_effort": "xhigh"}
  prompt: |
    [Round N update]

    Since your last review, we have:
    1. [Action 1]: [result]
    2. [Action 2]: [result]
    3. [Action 3]: [result]

    Updated results table:
    [paste metrics]

    Please re-score and re-assess. Are the remaining concerns addressed?
    Same format: Score, Verdict, Remaining Weaknesses, Minimum Fixes.
```

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