| 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: $ARGUMENTS
Constants
- MAX_ROUNDS = 4
- POSITIVE_THRESHOLD: score >= 6/10, or verdict contains "accept", "sufficient", "ready for submission"
- REVIEW_DOC:
review-stage/AUTO_REVIEW.md (cumulative log) (fall back to ./AUTO_REVIEW.md for legacy projects)
LLM Configuration
This skill uses any OpenAI-compatible API for external review via the llm-chat MCP server.
Configuration via MCP Server (Recommended)
Add to ~/.claude/settings.json:
{
"mcpServers": {
"llm-chat": {
"command": "/usr/bin/python3",
"args": ["/Users/yourname/.claude/mcp-servers/llm-chat/server.py"],
"env": {
"LLM_API_KEY": "your-api-key",
"LLM_BASE_URL": "https://api.deepseek.com/v1",
"LLM_MODEL": "deepseek-chat"
}
}
}
}
Supported Providers
| Provider | LLM_BASE_URL | LLM_MODEL |
|---|
| OpenAI | https://api.openai.com/v1 | gpt-4o, o3 |
| DeepSeek | https://api.deepseek.com/v1 | deepseek-chat, deepseek-reasoner |
| MiniMax | https://api.minimax.io/v1 | MiniMax-M2.7 |
| Kimi (Moonshot) | https://api.moonshot.cn/v1 | moonshot-v1-8k, moonshot-v1-32k |
| ZhiPu (GLM) | https://open.bigmodel.cn/api/paas/v4 | glm-4, glm-4-plus |
| SiliconFlow | https://api.siliconflow.cn/v1 | Qwen/Qwen2.5-72B-Instruct |
| 阿里云百炼 | https://dashscope.aliyuncs.com/compatible-mode/v1 | qwen-max |
| 零一万物 | https://api.lingyiwanwu.com/v1 | yi-large |
API Call Method
Primary: MCP Tool
mcp__llm-chat__chat:
prompt: |
[Review prompt content]
model: "deepseek-chat"
system: "You are a senior ML reviewer..."
Fallback: curl
curl -s "${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
- Check
review-stage/REVIEW_STATE.json for recovery (fall back to ./REVIEW_STATE.json if not found — legacy path)
- Read project context and prior reviews
- 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 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
- Set
review-stage/REVIEW_STATE.json status to "completed"
- 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:
