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Keeping track is impossible. This project:\n\n1. **Curates authoritative data** - LMArena Elo rankings, manual curation for video\u002Fimage\u002Faudio models\n2. **Updates daily** via GitHub Actions\n3. **Exports to JSON\u002FCSV\u002FSQLite** - Use in your own projects\n4. **Provides multiple interfaces** - Static files, REST API, or MCP server\n\n## Quick Start: Use the Data\n\n### Option 1: Download JSON\u002FCSV\n\n```bash\n# Latest data (updated daily)\ncurl -O https:\u002F\u002Fraw.githubusercontent.com\u002Fromancircus\u002Fsota-tracker-mcp\u002Fmain\u002Fdata\u002Fsota_export.json\ncurl -O https:\u002F\u002Fraw.githubusercontent.com\u002Fromancircus\u002Fsota-tracker-mcp\u002Fmain\u002Fdata\u002Fsota_export.csv\n```\n\n### Option 2: Clone and Query Locally\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fromancircus\u002Fsota-tracker-mcp.git\ncd sota-tracker-mcp\n\n# Query with sqlite3\nsqlite3 data\u002Fsota.db \"SELECT name, sota_rank FROM models WHERE category='llm_api' ORDER BY sota_rank LIMIT 10\"\n\n# List forbidden\u002Foutdated models\nsqlite3 data\u002Fsota.db \"SELECT name, reason, replacement FROM forbidden\"\n```\n\n### Option 3: Use with Claude Code (Recommended)\n\nThe recommended approach for Claude Code users is **static file embedding** (lower token cost than MCP):\n\n```bash\n# Set up daily auto-update of CLAUDE.md\ncp scripts\u002Fupdate_sota_claude_md.py ~\u002Fscripts\u002F\n\n# Enable systemd timer (runs at 6 AM daily)\nsystemctl --user enable --now sota-update.timer\n\n# Or run manually\npython ~\u002Fscripts\u002Fupdate_sota_claude_md.py --update\n```\n\nThis embeds a compact SOTA summary directly in your `~\u002F.claude\u002FCLAUDE.md` file.\n\n### Option 4: REST API\n\n```bash\n# Start the API server\nuvicorn rest_api:app --host 0.0.0.0 --port 8000\n\n# Query endpoints\ncurl \"http:\u002F\u002Flocalhost:8000\u002Fapi\u002Fv1\u002Fmodels?category=llm_api\"\ncurl \"http:\u002F\u002Flocalhost:8000\u002Fapi\u002Fv1\u002Fforbidden\"\ncurl \"http:\u002F\u002Flocalhost:8000\u002Fapi\u002Fv1\u002Fmodels\u002FFLUX.1-dev\u002Ffreshness\"\n```\n\n### Option 5: MCP Server (Optional)\n\nMCP support is available but disabled by default (higher token cost). To enable:\n\n```bash\n# Edit .mcp.json to add the server config\ncat > .mcp.json \u003C\u003C 'EOF'\n{\n  \"mcpServers\": {\n    \"sota-tracker\": {\n      \"command\": \"python\",\n      \"args\": [\"server.py\"]\n    }\n  }\n}\nEOF\n```\n\n## Data Sources\n\n| Source | Data | Update Frequency |\n|--------|------|------------------|\n| [LMArena](https:\u002F\u002Flmarena.ai\u002Fleaderboard) | LLM Elo rankings (6M+ human votes) | Daily |\n| [Artificial Analysis](https:\u002F\u002Fartificialanalysis.ai) | LLM benchmarks, pricing, speed | Daily |\n| [HuggingFace](https:\u002F\u002Fhuggingface.co) | Model downloads, trending | Daily |\n| Manual curation | Video, Image, Audio, Video2Audio models | As needed |\n\n## Categories\n\n| Category | Description | Top Models (Feb 2026) |\n|----------|-------------|----------------------|\n| `llm_api` | Cloud LLM APIs | Gemini 3 Pro, Grok 4.1, Claude Opus 4.5 |\n| `llm_local` | Local LLMs (GGUF) | Qwen3, Llama 3.3, DeepSeek-V3 |\n| `llm_coding` | Code-focused LLMs | Qwen3-Coder, DeepSeek-V3 |\n| `image_gen` | Image generation | Z-Image-Turbo, FLUX.2-dev, Qwen-Image |\n| `video` | Video generation | LTX-2, Wan 2.2, HunyuanVideo 1.5 |\n| `video2audio` | Video-to-audio (foley) | MMAudio V2 Large |\n| `tts` | Text-to-speech | ChatterboxTTS, F5-TTS |\n| `stt` | Speech-to-text | Whisper Large v3 |\n| `embeddings` | Vector embeddings | BGE-M3 |\n\n## REST API Endpoints\n\n| Endpoint | Description |\n|----------|-------------|\n| `GET \u002Fapi\u002Fv1\u002Fmodels?category=X` | Get SOTA for a category |\n| `GET \u002Fapi\u002Fv1\u002Fmodels\u002F:name\u002Ffreshness` | Check if model is current or outdated |\n| `GET \u002Fapi\u002Fv1\u002Fforbidden` | List outdated models to avoid |\n| `GET \u002Fapi\u002Fv1\u002Fcompare?model_a=X&model_b=Y` | Compare two models |\n| `GET \u002Fapi\u002Fv1\u002Frecent?days=30` | Models released in past N days |\n| `GET \u002Fapi\u002Fv1\u002Frecommend?task=chat` | Get recommendation for a task |\n| `GET \u002Fhealth` | Health check |\n\n## Run Your Own Scraper\n\n```bash\n# Install dependencies\npip install -r requirements.txt\npip install playwright\nplaywright install chromium\n\n# Run all scrapers\npython scrapers\u002Frun_all.py --export\n\n# Output:\n# data\u002Fsota_export.json\n# data\u002Fsota_export.csv\n# data\u002Flmarena_latest.json\n```\n\n## GitHub Actions (Auto-Update)\n\nThis repo uses GitHub Actions to:\n- **Daily**: Scrape all sources, update database, commit changes\n- **Weekly**: Create a tagged release with JSON\u002FCSV exports\n\nTo enable on your fork:\n1. Fork this repo\n2. Go to Settings → Actions → Enable workflows\n3. Data will auto-update daily at 6 AM UTC\n\n## File Structure\n\n```\nsota-tracker-mcp\u002F\n├── server.py                    # MCP server (optional)\n├── rest_api.py                  # REST API server\n├── init_db.py                   # Database initialization + seeding\n├── requirements.txt             # Dependencies\n├── data\u002F\n│   ├── sota.db                  # SQLite database\n│   ├── sota_export.json         # Full JSON export\n│   ├── sota_export.csv          # CSV export\n│   └── forbidden.json           # Outdated models list\n├── scrapers\u002F\n│   ├── lmarena.py               # LMArena scraper (Playwright)\n│   ├── artificial_analysis.py   # AA scraper (Playwright)\n│   └── run_all.py               # Unified runner\n├── fetchers\u002F\n│   ├── huggingface.py           # HuggingFace API\n│   └── cache_manager.py         # Smart caching\n└── .github\u002Fworkflows\u002F\n    └── daily-scrape.yml         # GitHub Actions workflow\n```\n\n## Contributing\n\nFound a model that's missing or incorrectly ranked?\n\n1. **For manual additions**: Edit `init_db.py` and submit a PR\n2. **For scraper improvements**: Edit files in `scrapers\u002F`\n3. **For new data sources**: Add a new scraper and update `run_all.py`\n\nSee [CONTRIBUTING.md](CONTRIBUTING.md) for full developer setup and PR process.\n\n## OpenCode \u002F Agents.md Integration\n\nThe repo now supports updating `agents.md` files for OpenCode agents:\n\n```bash\n# Update your agents.md with latest SOTA data\npython update_agents_md.py\n\n# Minimal version (top 1 model per category, lightweight)\npython update_agents_md.py --minimal\n\n# Custom categories and limit\npython update_agents_md.py --categories llm_local image_gen --limit 3\n\n# Force refresh from sources first\npython update_agents_md.py --refresh\n```\n\n### Automation\n\nAdd to your cron or systemd timer for daily updates:\n\n```cron\n# ~: crontab -e\n@daily python ~\u002FApps\u002Fsota-tracker-mcp\u002Fupdate_agents_md.py\n```\n\nOr systemd:\n\n```bash\n# ~\u002F.config\u002Fsystemd\u002Fuser\u002Fsota-update.service\n[Unit]\nDescription=Update SOTA models for agents\nAfter=network.target\n\n[Service]\nExecStart=%h\u002FApps\u002Fsota-tracker-mcp\u002Fupdate_agents_md.py\n\n[Install]\nWantedBy=default.target\n\n# ~\u002F.config\u002Fsystemd\u002Fuser\u002Fsota-update.timer\n[Unit]\nDescription=Daily SOTA data update\nOnCalendar=daily\nAccuracySec=1h\n\n[Install]\nWantedBy=timers.target\n\n# Enable\nsystemctl --user enable --now sota-update.timer\n```\n\nSee [CONTRIBUTING.md](CONTRIBUTING.md) for full setup guide\n\n## Data Attribution & Legal\n\nThis project aggregates **publicly available benchmark data** from third-party sources. We do not claim ownership of rankings, Elo scores, or benchmark results.\n\n### Data Sources (Used With Permission)\n\n| Source | Data | Permission |\n|--------|------|------------|\n| [LMArena](https:\u002F\u002Flmarena.ai) | Chatbot Arena Elo rankings | `robots.txt: Allow: \u002F` |\n| [Artificial Analysis](https:\u002F\u002Fartificialanalysis.ai) | LLM quality benchmarks | `robots.txt: Allow: \u002F` (explicitly allows AI crawlers) |\n| [HuggingFace](https:\u002F\u002Fhuggingface.co) | Model metadata, downloads | Public API |\n| [Open LLM Leaderboard](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fopen-llm-leaderboard\u002Fopen_llm_leaderboard) | Open-source LLM benchmarks | CC-BY license |\n\n### Disclaimer\n\n- All benchmark scores and rankings are the intellectual work of their respective sources\n- This project provides aggregation and tooling, not original benchmark data\n- Data is scraped once daily to minimize server load\n- If you are a data source and wish to be excluded, please open an issue\n\n### Fair Use\n\nThis project:\n- Aggregates factual data (not copyrightable)\n- Adds value through tooling (API server, unified format, forbidden list)\n- Attributes all sources with links\n- Does not compete commercially with sources\n- Respects robots.txt permissions\n\n## License\n\nMIT - See [LICENSE](LICENSE) for details.\n\nThe **code** in this repository is MIT licensed. 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Check compliance, read rules, log actions, query governance state.","https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fztsalexey\u002Fagent-constitution\u002FSKILL.md",[364,365],{"categoryNo":17,"categoryName":18,"taxonomyType":19,"taxonomyTypeDesc":20},{"categoryNo":366,"categoryName":367,"taxonomyType":264,"taxonomyTypeDesc":265},"01JBSEEDCAT000000000000037","律师与法务",[369,370,371,372,270,373,374,29,375,329,376,377,378,379,380,283,381,382,383],"agent constitution","constitution","contracts","tree","query","base","log","check","Rules","projects","compliance","actions","state","governance","github"]