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Agent skill

Auto Review Loop LLM

by AI4Scientist in AI4Scientist/nano-scientist

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

No licenceAuto-check: warningsAgent Workflows

Install Auto Review Loop LLM

The automated check flagged lines worth reading first. See the safety section below.

skills CLI
$ npx skills add AI4Scientist/nano-scientist --skill auto-review-loop-llm -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install AI4Scientist/nano-scientist auto-review-loop-llm --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/AI4Scientist/nano-scientist.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/auto-review-loop-llm .claude/skills/auto-review-loop-llm && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
auto-review-loop-llm
GitHub stars
128
Used in
3 other repos
Token cost
~1.8k tokens
SKILL.md length
389 words
Files
1
Skills in repo
74
Repo updated
First seen
Licence
None found

At a glance

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

  • Works in 3 steps: Check review-stage/REVIEW_STATE.json for… → Read project context and prior reviews → Initialize round counter
  • With auto review loop llm
  • SKILL.md covers Context: $ARGUMENTS, Constants, LLM Configuration and API Call Method, plus 5 more sections
  • Calls curl; reaches api.deepseek.com and api.openai.com; needs LLM_API_KEY

What it does

Auto Review Loop LLM is an agent skill from AI4Scientist/nano-scientist. 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".

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows, covering LLM API integration, MCP servers and Secrets management. It works with Model Context Protocol, OpenAI, Zhipu GLM and DeepSeek. The repository describes itself as: An autonomous research agent that turns a topic into a peer-reviewed technical report.

When your agent uses it

  • With auto review loop llm
  • Tasks that involve LLM API integration
  • Tasks that involve MCP servers

Example prompts

  • “auto review loop llm”
  • “llm review”
  • “/auto-review-loop-llm”

Requirements

  • A credential in LLM_API_KEY
  • Pre-approved tools (allowed-tools): Bash(*), Read, Grep, Glob, Write, Edit, Agent, Skill

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. Check review-stage/REVIEW_STATE.json for recovery *(fall back to ./REVIEW_STATE.json if not found — legacy path)*
  2. Read project context and prior reviews
  3. Initialize round counter

What it can do on your machine

Read from SKILL.md and the folder at commit 7132192. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash(*)
    • Read
    • Grep
    • Glob
    • Write
    • Edit
    • Agent
    • Skill

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • curl

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • api.deepseek.com
    • api.openai.com
    • api.minimax.io
    • api.moonshot.cn
    • open.bigmodel.cn
    • api.siliconflow.cn
    • dashscope.aliyuncs.com
    • api.lingyiwanwu.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • LLM_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Auto Review Loop LLM loads about 1.8k tokens when it runs. Until then it costs about 51 tokens; SKILL.md has 389 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~51
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: warnings

The automated check found patterns that need a careful read before installing.

  • WarningTells the agent its actions are pre-authorized / not to stop for confirmationSKILL.md:206
    t << 'EOF' > file`) to write in chunks. Do NOT ask the user for permission — just do it silently.
  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Bash(*), Read, Grep, Glob, Write, Edit, Agent, Skill

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

Without a licence we can't republish the file, so here is its outline and opening line. It has 389 words (~1,814 tokens).

“Autonomously iterate: review → implement fixes → re-review, until the external reviewer gives a positive assessment or MAX_ROUNDS is reached.”

— opening of SKILL.md by AI4Scientist
name
auto-review-loop-llm
allowed-tools
Bash(*), Read, Grep, Glob, Write, Edit, Agent, Skill
argument-hint
topic-or-scope

Read the full SKILL.md on GitHub

Files

Just SKILL.md in skills/auto-review-loop-llm of AI4Scientist/nano-scientist.

Open the folder on GitHubat commit 7132192

Used in 3 other repositories

We found 8 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 3 other GitHub owners. This page covers the copy in AI4Scientist/nano-scientist, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Auto Review Loop LLM next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Auto Review Loop LLM compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Auto Review Loop LLM this skillAI4Scientist/nano-scientist1283 repos~1.8kAutomated safety check: WarnNone
Codex Agent Provider for NanoClawnanocoai/nanoclaw31k—~2.3kAutomated safety check: NotesMIT
Atomic Chat MCP Tool for NanoClawnanocoai/nanoclaw31k—~2.6kAutomated safety check: NotesMIT
Openai Knowledgeopenai/openai-agents-python30k—~408Automated safety check: PassMIT
Openai Docs Skillaiskillstore/marketplace4301 repos~517Automated safety check: PassNone
Openai Docsaafqaq/codex-lb-enhanced1032 repos~861Automated safety check: PassApache-2.0

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Works with

Categories

Questions about Auto Review Loop LLM

What does Auto Review Loop LLM do?

Autonomous research review loop using any OpenAI-compatible LLM API. Auto Review Loop LLM is an agent skill from AI4Scientist/nano-scientist. Autonomous research review loop using any OpenAI-compatible LLM API.

When should I use Auto Review Loop LLM?

Auto Review Loop LLM fits situations like: with auto review loop llm; tasks that involve LLM API integration; tasks that involve MCP servers.

How do I install Auto Review Loop LLM in Claude Code?

Run `npx skills add AI4Scientist/nano-scientist --skill auto-review-loop-llm -a claude-code`. Or copy the skill folder (skills/auto-review-loop-llm in AI4Scientist/nano-scientist) into .claude/skills/auto-review-loop-llm in your project. Claude Code loads it when a task matches its description.

How do I install Auto Review Loop LLM in Codex?

Run `npx skills add AI4Scientist/nano-scientist --skill auto-review-loop-llm -a codex`. Or copy the skill folder (skills/auto-review-loop-llm in AI4Scientist/nano-scientist) into .agents/skills/auto-review-loop-llm in your project. Codex loads it when a task matches its description.

Can I use Auto Review Loop LLM in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add AI4Scientist/nano-scientist --skill auto-review-loop-llm -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/auto-review-loop-llm, .gemini/skills/auto-review-loop-llm, .github/skills/auto-review-loop-llm and .opencode/skills/auto-review-loop-llm in your project.

What does Auto Review Loop LLM need to run?

Going by SKILL.md and its folder, Auto Review Loop LLM needs the command-line tools its instructions call (curl) and credentials named LLM_API_KEY. Our summary lists: A credential in LLM_API_KEY. Its frontmatter pre-approves these tools: Bash(*), Read, Grep, Glob, Write, Edit, Agent, Skill.

Does Auto Review Loop LLM access the network?

SKILL.md names 8 domains. In commands or code: api.deepseek.com, api.openai.com, api.minimax.io, api.moonshot.cn, open.bigmodel.cn, api.siliconflow.cn, dashscope.aliyuncs.com and api.lingyiwanwu.com; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.

Is Auto Review Loop LLM safe to install?

Our automated static check of SKILL.md flagged 1 warning(s): tells the agent its actions are pre-authorized / not to stop for confirmation. Read the flagged lines before installing; the check is not a guarantee either way.

What licence does Auto Review Loop LLM use?

No licence was found for Auto Review Loop LLM or its repository. Without one, default copyright applies: ask the author before reusing or redistributing it.

How many tokens does Auto Review Loop LLM use?

About 1.8k tokens (SKILL.md is roughly 7.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Auto Review Loop LLM?

Skills that share tags, products or a category with Auto Review Loop LLM: Codex Agent Provider for NanoClaw (nanocoai/nanoclaw, 31k stars), Atomic Chat MCP Tool for NanoClaw (nanocoai/nanoclaw, 31k stars), Openai Knowledge (openai/openai-agents-python, 30k stars) and Openai Docs Skill (aiskillstore/marketplace, 430 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Auto Review Loop LLM?

AI4Scientist (a GitHub organization) maintains it in AI4Scientist/nano-scientist, which has 128 GitHub stars. The repository holds 74 skills in this directory. The repository was last updated on June 3, 2026.

Source: AI4Scientist/nano-scientist on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.