Agent skill

auto-review-loop-llm

Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with "auto review loop llm" or "llm review".

View SKILL.md on GitHub Repository
Stars 6,306
Forks 582

Install this agent skill to your Project

npx add-skill https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/auto-review-loop-llm

SKILL.md

Auto Review Loop (Generic LLM): 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)

LLM Configuration

This skill uses any OpenAI-compatible API for external review via the llm-chat MCP server.

Configuration via MCP Server (Recommended)

Add to ~/.claude/settings.json:

json
{
  "mcpServers": {
    "llm-chat": {
      "command": "/usr/bin/python3",
      "args": ["/Users/yourname/.claude/mcp-servers/llm-chat/server.py"],
      "env": {
        "LLM_API_KEY": "your-api-key",
        "LLM_BASE_URL": "https://api.deepseek.com/v1",
        "LLM_MODEL": "deepseek-chat"
      }
    }
  }
}

Supported Providers

Provider LLM_BASE_URL LLM_MODEL
OpenAI https://api.openai.com/v1 gpt-4o, o3
DeepSeek https://api.deepseek.com/v1 deepseek-chat, deepseek-reasoner
MiniMax https://api.minimax.io/v1 MiniMax-M2.7
Kimi (Moonshot) https://api.moonshot.cn/v1 moonshot-v1-8k, moonshot-v1-32k
ZhiPu (GLM) https://open.bigmodel.cn/api/paas/v4 glm-4, glm-4-plus
SiliconFlow https://api.siliconflow.cn/v1 Qwen/Qwen2.5-72B-Instruct
阿里云百炼 https://dashscope.aliyuncs.com/compatible-mode/v1 qwen-max
零一万物 https://api.lingyiwanwu.com/v1 yi-large

API Call Method

Primary: MCP Tool

mcp__llm-chat__chat:
  prompt: |
    [Review prompt content]
  model: "deepseek-chat"
  system: "You are a senior ML reviewer..."

Fallback: curl

bash
curl -s "${LLM_BASE_URL}/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer ${LLM_API_KEY}" \
  -d '{
    "model": "${LLM_MODEL}",
    "messages": [
      {"role": "system", "content": "You are a senior ML reviewer..."},
      {"role": "user", "content": "[review prompt]"}
    ],
    "max_tokens": 4096
  }'

State Persistence (Compact Recovery)

Persist state to REVIEW_STATE.json after each round:

json
{
  "round": 2,
  "status": "in_progress",
  "last_score": 5.0,
  "last_verdict": "not ready",
  "pending_experiments": [],
  "timestamp": "2026-03-15T10:00:00"
}

Write this file at the end of every Phase E (after documenting the round).

On completion, set "status": "completed".

Workflow

Initialization

  1. Check REVIEW_STATE.json for recovery
  2. Read project context and prior reviews
  3. Initialize round counter

Loop (up to MAX_ROUNDS)

Phase A: Review

If MCP available:

mcp__llm-chat__chat:
  system: "You are a senior ML reviewer (NeurIPS/ICML level)."
  prompt: |
    [Round N/MAX_ROUNDS of autonomous review loop]

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

    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
    4. State clearly: is this READY for submission? Yes/No/Almost

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

If MCP NOT available:

bash
curl -s "${LLM_BASE_URL}/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer ${LLM_API_KEY}" \
  -d '{
    "model": "${LLM_MODEL}",
    "messages": [
      {"role": "system", "content": "You are a senior ML reviewer (NeurIPS/ICML level)."},
      {"role": "user", "content": "[Full review prompt]"}
    ],
    "max_tokens": 4096
  }'

Phase B: Parse Assessment

CRITICAL: Save the FULL raw response verbatim. Then extract:

  • Score (numeric 1-10)
  • Verdict ("ready" / "almost" / "not ready")
  • Action items (ranked list of fixes)

STOP: If score >= 6 AND verdict contains "ready/almost"

Phase C: Implement Fixes

Priority: metric additions > reframing > new experiments

Phase D: Wait for Results

Monitor remote experiments

Phase E: Document Round

Append to AUTO_REVIEW.md:

markdown
## Round N (timestamp)

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

### Reviewer Raw Response

<details>
<summary>Click to expand full reviewer response</summary>

[Paste the COMPLETE raw response here — verbatim, unedited.]

</details>

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

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

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

Write REVIEW_STATE.json with current state.

Termination

  1. Set REVIEW_STATE.json status to "completed"
  2. Write final summary

Key Rules

  • Large file handling: If the Write tool fails due to file size, immediately retry using Bash (cat << 'EOF' > file) to write in chunks. Do NOT ask the user for permission — just do it silently.

  • Anti-hallucination citations: When adding references, NEVER fabricate BibTeX. Use DBLP → CrossRef → [VERIFY] chain. Do NOT generate BibTeX from memory.

  • Be honest about weaknesses

  • Implement fixes BEFORE re-reviewing

  • Document everything

  • Include previous context in round 2+ prompts

  • Prefer MCP tool over curl when available

Prompt Template for Round 2+

mcp__llm-chat__chat:
  system: "You are a senior ML reviewer (NeurIPS/ICML level)."
  prompt: |
    [Round N/MAX_ROUNDS of autonomous review loop]

    ## Previous Review Summary (Round N-1)
    - Previous Score: X/10
    - Previous Verdict: [ready/almost/not ready]
    - Previous Key Weaknesses: [list]

    ## Changes Since Last Review
    1. [Action 1]: [result]
    2. [Action 2]: [result]

    ## Updated Results
    [paste updated metrics/tables]

    Please re-score and re-assess:
    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
    4. State clearly: is this READY for submission? Yes/No/Almost

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

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

wanshuiyin/Auto-claude-code-research-in-sleep

ablation-planner

Use when main results pass result-to-claim (claim_supported=yes or partial) and ablation studies are needed for paper submission. Codex designs ablations from a reviewer's perspective, CC reviews feasibility and implements.

6,306 582
Explore
wanshuiyin/Auto-claude-code-research-in-sleep

paper-plan

Generate a structured paper outline from review conclusions and experiment results. Use when user says "写大纲", "paper outline", "plan the paper", "论文规划", or wants to create a paper plan before writing.

6,306 582
Explore
wanshuiyin/Auto-claude-code-research-in-sleep

idea-discovery-robot

Workflow 1 adaptation for robotics and embodied AI. Orchestrates robotics-aware literature survey, idea generation, novelty check, and critical review to go from a broad robotics direction to benchmark-grounded, simulation-first ideas. Use when user says "robotics idea discovery", "机器人找idea", "embodied AI idea", "机器人方向探索", "sim2real 选题", or wants ideas for manipulation, locomotion, navigation, drones, humanoids, or general robot learning.

6,306 582
Explore
wanshuiyin/Auto-claude-code-research-in-sleep

training-check

Periodically check WandB metrics during training to catch problems early (NaN, loss divergence, idle GPUs). Avoids wasting GPU hours on broken runs. Use when training is running and you want automated health checks.

6,306 582
Explore
wanshuiyin/Auto-claude-code-research-in-sleep

paper-plan

Generate a structured paper outline from review conclusions and experiment results. Use when user says "写大纲", "paper outline", "plan the paper", "论文规划", or wants to create a paper plan before writing.

6,306 582
Explore
wanshuiyin/Auto-claude-code-research-in-sleep

idea-discovery-robot

Workflow 1 adaptation for robotics and embodied AI. Orchestrates robotics-aware literature survey, idea generation, novelty check, and critical review to go from a broad robotics direction to benchmark-grounded, simulation-first ideas. Use when user says \"robotics idea discovery\", \"机器人找idea\", \"embodied AI idea\", \"机器人方向探索\", \"sim2real 选题\", or wants ideas for manipulation, locomotion, navigation, drones, humanoids, or general robot learning.

6,306 582
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results