# Auto Review Loop LLM

Autonomous research review loop using any OpenAI-compatible LLM API.

- Repository: https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep (18443 stars)
- Category: AI and agents
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill auto-review-loop-llm -g -a claude-code -y`
Codex: `npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill auto-review-loop-llm -g -a codex -y`
Cursor: `npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill auto-review-loop-llm -g -a cursor -y`
Gemini CLI: `npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill auto-review-loop-llm -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add wanshuiyin/Auto-claude-code-research-in-sleep`

## 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".

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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`](../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`:

```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

The rest: https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/blob/HEAD/skills/auto-review-loop-llm/SKILL.md