auto-review-loop-llm

auto-review-loop-llm is a skill for Claude Code from wanshuiyin/Auto-claude-code-research-in-sleep. It costs 47 tokens per session (2,247 once invoked), scanned B, original, MIT.

An automated research review cycle that uses an OpenAI-compatible language model API to review work, apply fixes, and review it again.

In plain words
What is it for?
Use it to improve research documents or project work through repeated external review. It can connect to an LLM chat server or use environment settings.
Why use it?
It reduces the need to repeat review and correction manually. The cycle stops after a positive review or after four rounds.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: reads .claude/ paths.

Not installable: its command points at a path on the author’s own machine, so it runs nowhere else. The line is /Users/yourname/.claude/mcp-servers/llm-chat/server.py.

Good fit Use it to improve research documents or project work through repeated external review. It can connect to an LLM chat server or use environment settings.

Compare 6 skills from other repositories ↓
About the project

ARIS is a collection of Markdown-based skills that define a workflow for autonomous machine-learning research, including idea discovery, experiment automation, and review loops. Researchers and AI coding agents use it across tools such as Claude Code, Codex, Cursor, and OpenClaw without depending on a single framework. The catalogue entries are ARIS workflow skills and agents.

wanshuiyin/Auto-claude-code-research-in-sleep · 15,970 stars · on GitHub

Install

Getting it into your agent

There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for auto-review-loop-llm

README.md
[![agentmods](https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/auto-review-loop-llm/github.svg)](https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/auto-review-loop-llm)
Your own site
<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/auto-review-loop-llm"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/auto-review-loop-llm/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for auto-review-loop-llm

Your own site · 80×15
<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/auto-review-loop-llm"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/auto-review-loop-llm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 47 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,247 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 2 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Socket pass 13 Apr 2026
  • Snyk fail 13 Apr 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00047 $0.02247
Opus 5 $0.00023 $0.01123
Sonnet 5 $0.00009 $0.00449
Haiku 4.5 $0.00005 $0.00225

Measured 12d ago against content hash f46c40b1a3ed, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade B, and why

auto-review-loop-llm scanned grade B with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Reads agent configuration directoriesmediumAgent snooping

.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.

Add to `~/.claude/settings.json`:

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

**Fallback: curl**
Origin

Copies of this mod

1 near-identical copy found in the catalogue:

skills/auto-review-loop-llm/SKILL.md · 260 lines

How it starts

The opening of the file, as written. The whole thing — 260 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

Read the full file on GitHub · 260 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 260 lines · 47 tokens per session scan B f46c40b1a3ed

Subscribe to this mod's changes

auto-review-loop-llm is a skill published in the GitHub repository wanshuiyin/Auto-claude-code-research-in-sleep (15,970 stars, last pushed 2d ago), licensed MIT. It adds 47 tokens to every session and 2,247 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 2 findings (reads agent configuration directories, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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