Summary

This project provides a Model Context Protocol (MCP) server, enabling any AI model or coding assistant to integrate Anthropic's Agent Skills. It offers intelligent search and retrieval of these skills via vector embeddings and semantic similarity, extending specialized AI capabilities across diverse AI ecosystems.

How It Works

A two-package architecture features a lightweight, instant-starting frontend and a feature-rich backend. The frontend handles immediate MCP communication, while the backend performs vector search using PyTorch and sentence-transformers. This design implements progressive disclosure for efficient skill loading and responsive user experience.

Quick Start & Requirements

For Cursor users, add via the Cursor Directory or ~/.cursor/mcp.json. Standalone execution uses uvx claude-skills-mcp, auto-downloading the backend. Custom configurations are JSON-based. Backend requires RAG dependencies; 60-120s init on first use, instant thereafter. Documentation links provided.

Highlighted Details

• Instant Frontend: Solves Cursor timeout (<5s startup).
• Semantic Skill Discovery: Vector embeddings enable intelligent skill search.
• Progressive Disclosure: Multi-level skill loading optimizes performance.
• Multi-Source Support: Integrates skills from GitHub/local directories (Anthropic, K-Dense AI scientific).
• Zero Configuration: Works out-of-the-box.
• Local Operation: No API keys; auto GitHub caching.

Maintenance & Community

Developed by K-Dense AI. Contributions via issues/PRs. Slack community for support. Centralized version management.

Licensing & Compatibility

Apache License 2.0 permits commercial use and integration into closed-source applications.

Limitations & Caveats

Initial backend download/setup (60-120s) due to RAG dependencies. Core skill set originates from Anthropic's framework.