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sota-tracker-mcpsota tracker MCP 安全

Agent Skill

sota-tracker-mcp 用于辅助安全审计、权限检查和凭据风险排查,适合在 OpenClaw 中需要复核安全边界、认证流程或敏感配置时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

58,165

周安装

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下载量

20,378
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:sota-tracker-mcp(sota tracker MCP 安全)
来源仓库:https://github.com/romancircus/sota-tracker-mcp
安装命令:
openclaw skills install sota-tracker-mcp
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install sota-tracker-mcp

简介

提供每日更新的权威数据和 API,跟踪 LMArena、Artificial Analysis 和 HuggingFace 等各个类别的最先进的 AI 模型。

SKILL.md

SOTA Tracker

The definitive open-source database of State-of-the-Art AI models.

Auto-updated daily from LMArena, Artificial Analysis, and HuggingFace.

Why This Exists

AI models are released weekly. Keeping track is impossible. This project:

  1. Curates authoritative data - LMArena Elo rankings, manual curation for video/image/audio models
  2. Updates daily via GitHub Actions
  3. Exports to JSON/CSV/SQLite - Use in your own projects
  4. Provides multiple interfaces - Static files, REST API, or MCP server

Quick Start: Use the Data

Option 1: Download JSON/CSV

# Latest data (updated daily)
curl -O https://raw.githubusercontent.com/romancircus/sota-tracker-mcp/main/data/sota_export.json
curl -O https://raw.githubusercontent.com/romancircus/sota-tracker-mcp/main/data/sota_export.csv

Option 2: Clone and Query Locally

git clone https://github.com/romancircus/sota-tracker-mcp.git
cd sota-tracker-mcp

# Query with sqlite3
sqlite3 data/sota.db "SELECT name, sota_rank FROM models WHERE category='llm_api' ORDER BY sota_rank LIMIT 10"

# List forbidden/outdated models
sqlite3 data/sota.db "SELECT name, reason, replacement FROM forbidden"

Option 3: Use with Claude Code (Recommended)

The recommended approach for Claude Code users is static file embedding (lower token cost than MCP):

# Set up daily auto-update of CLAUDE.md
cp scripts/update_sota_claude_md.py ~/scripts/

# Enable systemd timer (runs at 6 AM daily)
systemctl --user enable --now sota-update.timer

# Or run manually
python ~/scripts/update_sota_claude_md.py --update

This embeds a compact SOTA summary directly in your ~/.claude/CLAUDE.md file.

Option 4: REST API

# Start the API server
uvicorn rest_api:app --host 0.0.0.0 --port 8000

# Query endpoints
curl "http://localhost:8000/api/v1/models?category=llm_api"
curl "http://localhost:8000/api/v1/forbidden"
curl "http://localhost:8000/api/v1/models/FLUX.1-dev/freshness"

Option 5: MCP Server (Optional)

MCP support is available but disabled by default (higher token cost). To enable:

# Edit .mcp.json to add the server config
cat > .mcp.json << 'EOF'
{
  "mcpServers": {
    "sota-tracker": {
      "command": "python",
      "args": ["server.py"]
    }
  }
}
EOF

Data Sources

SourceDataUpdate Frequency
LMArenaLLM Elo rankings (6M+ human votes)Daily
Artificial AnalysisLLM benchmarks, pricing, speedDaily
HuggingFaceModel downloads, trendingDaily
Manual curationVideo, Image, Audio, Video2Audio modelsAs needed

Categories

CategoryDescriptionTop Models (Feb 2026)
llm_apiCloud LLM APIsGemini 3 Pro, Grok 4.1, Claude Opus 4.5
llm_localLocal LLMs (GGUF)Qwen3, Llama 3.3, DeepSeek-V3
llm_codingCode-focused LLMsQwen3-Coder, DeepSeek-V3
image_genImage generationZ-Image-Turbo, FLUX.2-dev, Qwen-Image
videoVideo generationLTX-2, Wan 2.2, HunyuanVideo 1.5
video2audioVideo-to-audio (foley)MMAudio V2 Large
ttsText-to-speechChatterboxTTS, F5-TTS
sttSpeech-to-textWhisper Large v3
embeddingsVector embeddingsBGE-M3

REST API Endpoints

EndpointDescription
GET /api/v1/models?category=XGet SOTA for a category
GET /api/v1/models/:name/freshnessCheck if model is current or outdated
GET /api/v1/forbiddenList outdated models to avoid
GET /api/v1/compare?model_a=X&model_b=YCompare two models
GET /api/v1/recent?days=30Models released in past N days
GET /api/v1/recommend?task=chatGet recommendation for a task
GET /healthHealth check

Run Your Own Scraper

# Install dependencies
pip install -r requirements.txt
pip install playwright
playwright install chromium

# Run all scrapers
python scrapers/run_all.py --export

# Output:
# data/sota_export.json
# data/sota_export.csv
# data/lmarena_latest.json

GitHub Actions (Auto-Update)

This repo uses GitHub Actions to:

  • Daily: Scrape all sources, update database, commit changes
  • Weekly: Create a tagged release with JSON/CSV exports

To enable on your fork:

  1. Fork this repo
  2. Go to Settings → Actions → Enable workflows
  3. Data will auto-update daily at 6 AM UTC

File Structure

sota-tracker-mcp/
├── server.py                    # MCP server (optional)
├── rest_api.py                  # REST API server
├── init_db.py                   # Database initialization + seeding
├── requirements.txt             # Dependencies
├── data/
│   ├── sota.db                  # SQLite database
│   ├── sota_export.json         # Full JSON export
│   ├── sota_export.csv          # CSV export
│   └── forbidden.json           # Outdated models list
├── scrapers/
│   ├── lmarena.py               # LMArena scraper (Playwright)
│   ├── artificial_analysis.py   # AA scraper (Playwright)
│   └── run_all.py               # Unified runner
├── fetchers/
│   ├── huggingface.py           # HuggingFace API
│   └── cache_manager.py         # Smart caching
└── .github/workflows/
    └── daily-scrape.yml         # GitHub Actions workflow

Contributing

Found a model that's missing or incorrectly ranked?

  1. For manual additions: Edit init_db.py and submit a PR
  2. For scraper improvements: Edit files in scrapers/
  3. For new data sources: Add a new scraper and update run_all.py

See CONTRIBUTING.md for full developer setup and PR process.

OpenCode / Agents.md Integration

The repo now supports updating agents.md files for OpenCode agents:

# Update your agents.md with latest SOTA data
python update_agents_md.py

# Minimal version (top 1 model per category, lightweight)
python update_agents_md.py --minimal

# Custom categories and limit
python update_agents_md.py --categories llm_local image_gen --limit 3

# Force refresh from sources first
python update_agents_md.py --refresh

Automation

Add to your cron or systemd timer for daily updates:

# ~: crontab -e
@daily python ~/Apps/sota-tracker-mcp/update_agents_md.py

Or systemd:

# ~/.config/systemd/user/sota-update.service
[Unit]
Description=Update SOTA models for agents
After=network.target

[Service]
ExecStart=%h/Apps/sota-tracker-mcp/update_agents_md.py

[Install]
WantedBy=default.target

# ~/.config/systemd/user/sota-update.timer
[Unit]
Description=Daily SOTA data update
OnCalendar=daily
AccuracySec=1h

[Install]
WantedBy=timers.target

# Enable
systemctl --user enable --now sota-update.timer

See CONTRIBUTING.md for full setup guide

Data Attribution & Legal

This project aggregates publicly available benchmark data from third-party sources. We do not claim ownership of rankings, Elo scores, or benchmark results.

Data Sources (Used With Permission)

SourceDataPermission
LMArenaChatbot Arena Elo rankingsrobots.txt: Allow: /
Artificial AnalysisLLM quality benchmarksrobots.txt: Allow: / (explicitly allows AI crawlers)
HuggingFaceModel metadata, downloadsPublic API
Open LLM LeaderboardOpen-source LLM benchmarksCC-BY license

Disclaimer

  • All benchmark scores and rankings are the intellectual work of their respective sources
  • This project provides aggregation and tooling, not original benchmark data
  • Data is scraped once daily to minimize server load
  • If you are a data source and wish to be excluded, please open an issue

Fair Use

This project:

  • Aggregates factual data (not copyrightable)
  • Adds value through tooling (API server, unified format, forbidden list)
  • Attributes all sources with links
  • Does not compete commercially with sources
  • Respects robots.txt permissions

License

MIT - See LICENSE for details.

The code in this repository is MIT licensed. The data belongs to its respective sources (see attribution above).

适合场景

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02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

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需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

81.89%
按下载量换算16,688

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

未展示

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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