https://skill-mcp.jigneshponamwar.workers.dev/mcpEvery approach before skills-mcp forces the same trade-off between agent capability and context efficiency. The model gets everything or nothing.
context window exhausted before first user message
Embedding 30 expert procedures into every system prompt costs 60-80k tokens before the agent sends a single message. Most of that context is never used.
Every project gets its own skill file copies. One update means manually syncing N files across M projects, with no version control and no diffing.
Claude Code, Cursor, Windsurf, Cline: different config formats, different file paths, different scopes. The same procedure files multiply into incompatible formats.
Static files can't score themselves. No way to retrieve the procedure best suited to the task at hand. It's all procedures or none.
What if the agent could request exactly the skill it needs, the moment it needs it?
skills-mcp serves knowledge in layers. The agent always starts with the lightest context and escalates only when the task demands it.
The agent calls skills_find_relevant() with a plain description. Qdrant returns the highest-scoring match by cosine similarity against embedded descriptions.
skills_find_relevant("stripe webhooks")
→ 0.97 stripe-integration [tier-1]Returns frontmatter only. ~200 tokens.
When ready to act, the agent calls skills_get_body(). The complete step-by-step procedure is returned for exactly one skill, not all 407.
skills_get_body("stripe-integration") ← latest
skills_get_body("stripe-integration", version="1.2") ← pinned
Returns a deprecation_notice if the skill has been superseded.Full procedure + patterns. ~2-4k tokens.
For complex tasks, tier-3 tools pull curated docs, API references, and scripts. Only loaded when the skill body explicitly references them in the manifest.
skills_get_reference("stripe-integration", "WEBHOOKS.md")
→ Verification spec · Security checklistOnly loaded when the body references it.
My hosted instance is live. Connect it to your AI agent right now.
{ "mcpServers": { "skill-mcp": { "transport": "streamable-http", "url": "https://skill-mcp.jigneshponamwar.workers.dev/mcp" } } }
Paste this config into your AI tool’s MCP settings. The server is already running no deployment needed. Per-IP rate limiting: 60 requests/min (configurable via RATE_LIMIT_RPM). Exceeding the limit returns HTTP 429.
33 core skills ship with the repo. 374 expanded skills available via the hosted instance or by running the import script. Sourced from Anthropic, Vercel, Prisma, Expo, Microsoft, Trail of Bits, Neo4j, and community contributors.
> 0.6Strong match: load skill body0.4-0.6Review description, may be relevant< 0.4No match: skip this skillThresholds validated against a 120-query eval dataset (90 positive × 407 skills + 30 true negatives). Run make calibrate to recalibrate.
test-writer
pytest, Jest, Go test suites with full edge case coverage and mocking patterns.
Write test suites
claude-api
Anthropic SDK: tool use, streaming, vision, prompt caching, extended thinking, batch.
Integrate Claude
docker-containerization
Production Dockerfiles, multi-stage builds, Docker Compose, security hardening.
Container management
stripe-integration
Checkout Sessions, webhooks, subscriptions, Connect (Accounts v2), security checklist.
Payment integration
django-web-framework
Django ORM, views, URL routing, templates, auth, middleware, and deployment patterns.
Build web applications
vuejs-framework
Single-file components, Composition API, Vue Router, Pinia state management, best practices.
Build single-page applications
Expanded from 33 core skills to 407 total by importing from the skills.sh ecosystem. 33 bundled in the repo, 407 on the hosted instance, expandable with one command.
33 core skills ship with the repo. 374 expanded skills from Anthropic, Vercel, Prisma, Expo, Microsoft, Trail of Bits, Neo4j, Solana, and community contributors.
Run python scripts/import_skills.py to import 374 additional skills from skills.sh into your self-hosted instance.
Skills organized across Backend, Frontend, Mobile, Databases, Cloud, AI, Security, Design, Auth, Marketing, Web3, and more.
Three paths to a running server. Cloudflare free tier for production. Docker for local-only or air-gapped use.
One command does everything: checks prerequisites, creates .env, installs deps, seeds Qdrant, pushes secrets, deploys the Worker.
.\scripts\setup.ps1
bash scripts/setup.sh
make setup
The wizard prompts for credentials at the right moment. Your server is live once it completes.
All platforms use the same SSE transport config. Replace the URL with your deployed Worker URL, or use the hosted instance above to try immediately without deploying.
{
"mcpServers": {
"skill-mcp": {
"transport": "streamable-http",
"url": "https://skill-mcp.jigneshponamwar.workers.dev/mcp"
}
}
}Replace the URL with your deployed Worker URL. Use the hosted instance to try without deploying.
Drop a platform instruction file into your project root. It teaches the agent the 3-tier skill workflow: when to call skills_find_relevant, how to interpret scores, and when to load tier-3 resources.
cp master-skill/platforms/claude-code/CLAUDE.md ./CLAUDE.mdcp master-skill/platforms/cursor/.cursorrules ./.cursorrulescp master-skill/platforms/windsurf/.windsurfrules ./.windsurfrulescp master-skill/platforms/cline/.clinerules ./.clinerulescp -r master-skill/platforms/copilot/.github ./.githubcp master-skill/platforms/codex/AGENTS.md ./AGENTS.mdcp -r master-skill/platforms/antigravity/.agents ./.agentscp master-skill/platforms/aider/CONVENTIONS.md ./CONVENTIONS.mdAfter copying, edit the file and replace the placeholder Worker URL with your actual deployed URL.