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.
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.
[](https://agentmods.dev/skills/raja21068/autoresearch/auto-review-loop-llm)<a href="https://agentmods.dev/skills/raja21068/autoresearch/auto-review-loop-llm"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/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.
<a href="https://agentmods.dev/skills/raja21068/autoresearch/auto-review-loop-llm"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/auto-review-loop-llm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00047 | $0.02032 |
| Opus 5 | $0.00023 | $0.01016 |
| Sonnet 5 | $0.00009 | $0.00406 |
| Haiku 4.5 | $0.00005 | $0.00203 |
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 8d 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** This is a copy
89% identical to auto-review-loop-llm — 20 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 250 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
Autonomously iterate: review → implement fixes → re-review, until the external reviewer gives a positive assessment or MAX_ROUNDS is reached.
Context: $ARGUMENTS
Constants
- MAX_ROUNDS = 4
- POSITIVE_THRESHOLD: score >= 6/10, or verdict contains "accept", "sufficient", "ready for submission"
- REVIEW_DOC:
review-stage/AUTO_REVIEW.md(cumulative log) (fall back to./AUTO_REVIEW.mdfor legacy projects)
LLM Configuration
This skill uses any OpenAI-compatible API for external review via the llm-chat MCP server.
Configuration via MCP Server (Recommended)
Add to ~/.claude/settings.json:
{
"mcpServers": {
"llm-chat": {
"command": "/usr/bin/python3",
"args": ["/Users/yourname/.claude/mcp-servers/llm-chat/server.py"],
"env": {
"LLM_API_KEY": "your-api-key",
"LLM_BASE_URL": "https://api.deepseek.com/v1",
"LLM_MODEL": "deepseek-chat"
}
}
}
}
Supported Providers
| Provider | LLM_BASE_URL | LLM_MODEL |
|---|---|---|
| OpenAI | https://api.openai.com/v1 |
gpt-4o, o3 |
| DeepSeek | https://api.deepseek.com/v1 |
deepseek-chat, deepseek-reasoner |
| MiniMax | https://api.minimax.io/v1 |
MiniMax-M2.7 |
| Kimi (Moonshot) | https://api.moonshot.cn/v1 |
moonshot-v1-8k, moonshot-v1-32k |
| ZhiPu (GLM) | https://open.bigmodel.cn/api/paas/v4 |
glm-4, glm-4-plus |
| SiliconFlow | https://api.siliconflow.cn/v1 |
Qwen/Qwen2.5-72B-Instruct |
| 阿里云百炼 | https://dashscope.aliyuncs.com/compatible-mode/v1 |
qwen-max |
| 零一万物 | https://api.lingyiwanwu.com/v1 |
yi-large |
API Call Method
Primary: MCP Tool
mcp__llm-chat__chat:
prompt: |
[Review prompt content]
model: "deepseek-chat"
system: "You are a senior ML reviewer..."
Fallback: curl
curl -s "${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
}'
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.
- 8d ago First seen · 250 lines · 47 tokens per session scan B 01da65516b19
auto-review-loop-llm is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 47 tokens to every session and 2,032 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). It is 89% identical to auto-review-loop-llm, differing in 20 lines, and is treated as a copy.
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