auto-review-loop-llm
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\".
npx skills add majiayu000/claude-skill-registry --skill auto-review-loop-llm-wanshuiyin-auto-claude-code-res --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 `~/.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` |
- Context: $ARGUMENTS
- Constants
- LLM Configuration
- Configuration via MCP Server (Recommended)
- Supported Providers
- API Call Method
- State Persistence (Compact Recovery)
- Workflow
- Initialization
- Loop (up to MAXROUNDS)
- Termination
- Key Rules
- Prompt Template for Round 2+
- Output Protocols
curl -s "${LLM_BASE_URL}/chat/completions" \What does the auto-review-loop-llm skill do?
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\".
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill auto-review-loop-llm-wanshuiyin-auto-claude-code-res --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.