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Claude Skills MCP Server

by K-Dense-AI

Claude Skills MCP Server

A Model Context Protocol (MCP) server that provides intelligent search capabilities for discovering relevant Claude Agent Skills using vector embeddings and semantic similarity.

An open-source project by - creators of autonomous AI scientists for scientific research.

This MCP server enables Claude to intelligently search and retrieve skills from our curated Claude Scientific Skills repository and other skill sources. If you want substantially more advanced capabilities, compute infrastructure, and enterprise-ready AI scientist offerings, check out K-Dense AI's commercial platform.

Features

  • 🔍 Semantic Search: Find the most relevant Claude Agent Skills using vector embeddings

  • 🚀 Zero Configuration: Works out of the box with curated scientific skills

  • 🌐 GitHub Integration: Load skills directly from GitHub repositories

  • 📁 Local Skills: Support for local skill directories

  • Fast: Local embeddings with sentence-transformers (no API keys needed)

  • 🔧 Configurable: Customize skill sources, embedding models, and search parameters

Quick Start

Using uvx (Recommended)

Run the server with default configuration (no installation required):

uvx claude-skills-mcp

This automatically loads skills from the K-Dense AI Scientific Skills repository - a curated collection of 70+ scientific skills for bioinformatics, cheminformatics, and scientific analysis.

With Custom Configuration

  1. Generate an example configuration file:

uvx claude-skills-mcp --example-config > config.json
  1. Edit config.json to customize your skill sources

  2. Run with your configuration:

uvx claude-skills-mcp --config config.json

Architecture

The server consists of five core components:

1. Configuration System (config.py)

  • Default configuration with K-Dense-AI scientific skills

  • JSON-based config loading

  • Fallback to defaults if config unavailable

  • Example config generator

2. Skill Loader (skill_loader.py)

  • GitHub repository loading via API (no authentication required)

  • Local directory scanning

  • YAML frontmatter parsing

  • Support for both direct skills and Claude Code plugin repositories

  • Robust error handling (network issues, missing files, etc.)

3. Search Engine (search_engine.py)

  • Sentence-transformers for local embeddings (all-MiniLM-L6-v2)

  • Vector indexing at startup for fast queries

  • Cosine similarity search

  • Configurable top-K results

4. MCP Server (server.py)

  • Standard MCP protocol implementation

  • One tool: search_skills

  • Stdio transport for easy integration

  • Formatted output with relevance scores

5. Entry Point (__main__.py)

  • CLI argument parsing

  • Async server lifecycle

  • Comprehensive error handling

  • Logging configuration

Configuration

The server uses a JSON configuration file to specify skill sources and search parameters.

Default Configuration

If no config file is specified, the server uses these defaults:

{ "skill_sources": [ { "type": "github", "url": "https://github.com/K-Dense-AI/claude-scientific-skills" } ], "embedding_model": "all-MiniLM-L6-v2", "default_top_k": 3 }

Example Custom Configuration

{ "skill_sources": [ { "type": "github", "url": "https://github.com/K-Dense-AI/claude-scientific-skills" }, { "type": "github", "url": "https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-thinking" }, { "type": "github", "url": "https://github.com/anthropics/claude-cookbooks", "subpath": "skills/custom_skills" }, { "type": "local", "path": "~/.claude/skills" } ], "embedding_model": "all-MiniLM-L6-v2", "default_top_k": 3 }

Configuration Options

  • skill_sources: Array of skill source configurations

    • type: Either "github" or "local"

    • url: GitHub repository URL (for github type). Supports:

      • Base repo: https://github.com/owner/repo

      • With subpath: https://github.com/owner/repo/tree/branch/subpath

    • subpath: Optional subdirectory within the repo (alternative to URL-based subpath)

    • path: Local directory path (for local type)

  • embedding_model: Name of the sentence-transformers model (default: "all-MiniLM-L6-v2")

  • default_top_k: Default number of skills to return (default: 3)

MCP Tool: search_skills

The server exposes one MCP tool for searching skills.

Input Parameters

  • task_description (required): Description of the task you want to accomplish

  • top_k (optional): Number of skills to return (default: 3, max: 20)

Output

Returns the most relevant skills with:

  • Skill name and description

  • Full SKILL.md content

  • Source URL or path

  • Relevance score (0-1, higher is better)

Example Usage in Claude

When this MCP server is connected, Claude can use:

I need help analyzing RNA sequencing data

Claude will invoke search_skills with this task description and receive the most relevant skills from the indexed sources.

Skill Format

The server searches for SKILL.md files with the following format:

--- name: Skill Name description: Brief description of what this skill does --- # Skill Name [Full skill content in Markdown...]

Technical Details

Dependencies

  • mcp>=1.0.0 - Model Context Protocol

  • sentence-transformers>=2.2.0 - Vector embeddings

  • numpy>=1.24.0 - Numerical operations

  • httpx>=0.24.0 - HTTP client for GitHub API

Python Version

  • Requires: Python 3.12 (not 3.13)

  • Dependencies are automatically managed by uv/uvx

Performance

  • Startup time: ~5-10 seconds (loads model and indexes skills)

  • Query time: <1 second for vector search

  • Memory usage: ~500MB (embedding model + indexed skills)

  • First run: Downloads ~100MB embedding model (cached thereafter)

How It Works

  1. Startup: Loads skills from configured GitHub repos and local directories

  2. Indexing: Generates vector embeddings for all skill descriptions using sentence-transformers

  3. Search: When a task description is received:

    • Generates embedding for the query

    • Computes cosine similarity with all indexed skills

    • Returns top-K most similar skills with full content

Supported Skill Sources

GitHub Repositories

The server can load skills from:

  • Direct skill folders (containing SKILL.md)

  • Claude Code plugin repositories (with .claude-plugin/marketplace.json)

Examples:

Local Directories

Any local directory containing SKILL.md files in subdirectories.

Error Handling

The server is designed to be resilient:

  • If a local folder is inaccessible, it logs a warning and continues

  • If a GitHub repo fails to load, it tries alternate branches and continues

  • If no skills are loaded, the server exits with an error message

Development

Installation from Source

git clone https://github.com/your-org/claude-skills-mcp.git cd claude-skills-mcp uv sync

Running in Development

uv run claude-skills-mcp

Running with Verbose Logging

uvx claude-skills-mcp --verbose

Running Tests

# Run all tests (with coverage - runs automatically) uv run pytest tests/ # Run only unit tests (fast) uv run pytest tests/ -m "not integration" # Run local demo (creates temporary skills) uv run pytest tests/test_integration.py::test_local_demo -v -s # Run repository demo (loads from K-Dense-AI scientific skills) uv run pytest tests/test_integration.py::test_repo_demo -v -s # Generate HTML coverage report uv run pytest tests/ --cov-report=html open htmlcov/index.html

Note: Coverage reporting is enabled by default. All test runs show coverage statistics.

See Testing Guide for more details.

Command Line Options

uvx claude-skills-mcp [OPTIONS] Options: --config PATH Path to configuration JSON file --example-config Print example configuration and exit --verbose, -v Enable verbose logging --help Show help message

How to Contribute

  1. Fork the repository

  2. Create a feature branch

  3. Make your changes

  4. Run tests: uv run pytest tests/ -v

  5. Submit a pull request

All contributions are welcome! Please ensure tests pass before submitting.

Documentation

Learn More

License

This project is licensed under the PolyForm Noncommercial License 1.0.0.

Required Notice: Copyright K-Dense AI (https://k-dense.ai)

For commercial use or licensing inquiries, please contact K-Dense AI at contact@k-dense.ai.


Status: Ready for testing and distribution
Version: 0.1.0

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