auto-review-loop-llm

name	auto-review-loop-llm
description	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".
argument-hint	["topic-or-scope"]
allowed-tools	Bash(*), Read, Grep, Glob, Write, Edit, Agent, Skill

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:

{
  "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.chat/v1	MiniMax-M2.5
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

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:

{
  "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:

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:

## 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

• 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.