Skills MCP Server
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Skills MCP ServerInfer skills from this resume: Senior Python developer with Kubernetes."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Skills MCP Server
A FastMCP server that exposes the skills-engine taxonomy as MCP tools — so any MCP client (Claude, Cursor, custom agents) can infer, normalize, and search skills, and inspect lifecycle governance.
Tools
Tool | Purpose |
| extract + resolve skills from JDs, resumes, tasks, reflections |
| map a raw label to custom → external → master tiers |
| semantic search across all tiers |
| counts by tier + lifecycle status |
| proposed actions with reasons (half-life policy) |
Related MCP server: semantic-runtime
Run
pip install -r requirements.txt
uvicorn skills_engine.api.app:app --reload # in the skills-engine repo, port 8000
export SKILLS_ENGINE_URL=http://127.0.0.1:8000
python src/skills_mcp/server.py # stdio MCP transportWire it into any MCP client config:
{
"mcpServers": {
"skills-engine": {
"command": "python",
"args": ["src/skills_mcp/server.py"]
}
}
}Tests
Tool functions are plain callables registered onto the FastMCP instance, so tests monkeypatch the transport
layer (_get/_post) and assert routing, payloads, and registration without a live engine.
MIT
This server cannot be deployed
Maintenance
Related MCP Connectors
Search and discover Agent Skills from the skills.sh registry. Powered by HAPI MCP server.
The governed runtime for agent skills. Search the catalog and inspect a skill before running it.
A registry of 5,900+ peer-authored skills any MCP agent can search and load on demand.
Governed AI agent skills — one library, distributed to devs and exposed to remote agents over MCP.
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