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SOTA Tracker (Claw)

Provides daily-updated, authoritative rankings and metadata of state-of-the-art AI models aggregated from leading sources via JSON, API, or local queries.

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Provides daily-updated, authoritative rankings and metadata of state-of-the-art AI models aggregated from leading sources via JSON, API, or local queries.

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Install for OpenClaw

Quick setup

Download the package from Yavira.
Extract the archive and review SKILL.md first.
Import or place the package into your OpenClaw setup.

Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: BACKLOG.md, CHANGELOG.md, CLAUDE.md, CONTRIBUTING.md, EVALUATION.md, README.md

Validation

Use the Yavira download entry.
Review SKILL.md after the package is downloaded.
Confirm the extracted package contains the expected setup assets.

Install with your agent

Agent handoff

Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.

Download the package from Yavira.
Extract it into a folder your agent can access.
Paste one of the prompts below and point your agent at the extracted folder.

New install

I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.

Upgrade existing

I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.

Open Send to Agent page Open JSON manifest Open Markdown brief

Trust & source

Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.0.1

Provenance

Publisher: romancircus
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 21 sections Open source page

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

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 FrequencyLMArenaLLM Elo rankings (6M+ human votes)DailyArtificial AnalysisLLM benchmarks, pricing, speedDailyHuggingFaceModel downloads, trendingDailyManual curationVideo, Image, Audio, Video2Audio modelsAs needed

REST API Endpoints

EndpointDescriptionGET /api/v1/models?category=XGet SOTA for a categoryGET /api/v1/models/:name/freshnessCheck if model is current or outdatedGET /api/v1/forbiddenList outdated models to avoidGET /api/v1/compare?model_a=X&model_b=YCompare two modelsGET /api/v1/recent?days=30Models released in past N daysGET /api/v1/recommend?task=chatGet recommendation for a taskGET /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: Fork this repo Go to Settings → Actions → Enable workflows 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? For manual additions: Edit init_db.py and submit a PR For scraper improvements: Edit files in scrapers/ 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)

SourceDataPermissionLMArenaChatbot Arena Elo rankingsrobots.txt: Allow: /Artificial AnalysisLLM quality benchmarksrobots.txt: Allow: / (explicitly allows AI crawlers)HuggingFaceModel metadata, downloadsPublic APIOpen 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).

Category context

Code helpers, APIs, CLIs, browser automation, testing, and developer operations.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

6 Docs

BACKLOG.md Docs
CHANGELOG.md Docs
CLAUDE.md Docs
CONTRIBUTING.md Docs
EVALUATION.md Docs
README.md Docs