---

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, Skill

Auto Review Loop (Generic LLM): Autonomous Research Improvement

🔒 Do not wrap this skill in /loop, /schedule, or CronCreate. Like /auto-review-loop, it already loops internally (review → fix → re-review), feeding each round's prior-round summary into the next review prompt (the backend is a stateless per-round API/MCP call, not a shared thread). An external timer re-enters from the top each tick, dropping that accumulated context and firing the verdict on wall-clock time instead of on artifact change — zero new signal, full token cost. Schedule the external wait that precedes it, not the verdict. See shared-references/external-cadence.md.

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 AND verdict ∈ {"ready", "almost"} — both must hold, matching the operative STOP check below. Verdict vocabulary is {"ready", "almost", "not ready"}. (Earlier wording used or and a stale verdict set; the AND form is authoritative.)
• REVIEW_DOC: review-stage/AUTO_REVIEW.md (cumulative log) (fall back to ./AUTO_REVIEW.md for legacy projects)

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.io/v1 | MiniMax-M3 | | 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-stage/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-stage/REVIEW_STATE.json for recovery (fall back to ./REVIEW_STATE.json if not found — legacy path)
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 ∈ {"ready", "almost"} (exact — "not ready" does NOT qualify)

Phase C: Implement Fixes

Priority: metric additions > reframing > new experiments

Phase D: Wait for Results

Monitor remote experiments

Phase E: Document Round

Append to review-stage/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-stage/REVIEW_STATE.json with current state.

Termination

1. Set review-stage/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.

Output Protocols

Follow these shared protocols for all output files:

• Output Versioning Protocol — write timestamped file first, then copy to fixed name
• Output Manifest Protocol — log every output to MANIFEST.md
• Output Language Protocol — respect the project's language setting