lix965996-art/mmm/auto-review-loop-llm

Name: auto-review-loop-llm
Author: lix965996-art

Trust tier 3

community

By lix965996-art
AI/ML
lix965996-art/mmm
resources/app/skills/skills-codex/auto-review-loop-llm/SKILL.md
Updated 18 Oct 2026
Version 1.0.01 version recorded

Source check pending

Checking that the SKILL.md file is still in the repository.

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\".

Install

npx vskill@latest install lix965996-art/mmm/auto-review-loop-llm

SKILL.md

The same SKILL.md was published earlier as pinkpixel-dev/skills-collection-2/auto-review-loop-llm.

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 ~/.codex/settings.json:

json

{
  "mcpServers": {
    "llm-chat": {
      "command": "/usr/bin/python3",
      "args": ["/Users/yourname/.codex/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:
  message: |
    [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

Check `REVIEW_STATE.json` for recovery
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)."
  message: |
    [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

Set 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.

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)."
  message: |
    [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.

Security

Security scanPassed, 90 of 100Pattern checks on the skill's files.
Model reviewPassed

See the full security report

Quality evaluation

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Source

SKILL.md in the repositoryCheckingresources/app/skills/skills-codex/auto-review-loop-llm/SKILL.md

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