"A server for finding information about skills and skill development" matching MCP servers:

Serves any directory of Agent Skills (SKILL.md folders) over MCP, exposing skills/list, skills/get, and skill:// resources so clients can enumerate and retrieve each skill's frontmatter and files. Every returned file comes with a real SHA-256 digest, letting clients verify content rather than merely trust it.

Enables AI clients to discover, search, retrieve, and read supporting files from a centrally published, versioned skill registry through four read-only MCP tools.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Enables MCP hosts to discover, browse, and load Agent Skills from one or many SKILL.md libraries over the Model Context Protocol, with namespaced libraries, search/list/get tools, progressive disclosure, and a digest-verified pull client for syncing skills to disk.

A self-hostable semantic skill registry for AI agents, providing searchable, versioned, and verified skills over a single MCP endpoint with per-agent private vaults and trust tiers.

A graph-based MCP server that discovers, resolves dependencies, and installs the minimal set of Claude Code components—including skills, agents, and commands—tailored to specific tasks.

Enables AI agents to semantically search and retrieve relevant skills from a local Markdown skill database, reducing context waste by loading only needed skills through tiered detail levels and tool calls.

Semantic, on-demand skill retrieval for Claude Code that saves tokens and improves skill discovery by replacing the native skill listing with vector embedding search.

Enables Claude to request and receive skills from a database on demand, allowing agents to fetch step-by-step instructions for tasks like lead research while keeping user credentials and execution on the user's own machine.

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

Enables intelligent discovery and management of Claude Skills through semantic search, ratings, favorites, and community curation. Provides tools to search, upload, rate, and organize skills with natural language queries and comprehensive metadata.

Bridges NotebookLM's research capabilities with Claude's content generation, enabling creation of notebooks, deep research queries, and structured content output from URLs, PDFs, or trending topics.

A proof of concept MCP server for Apache OFBiz

Enables dynamic discovery, search, and on-demand retrieval of local Claude Skills, making them accessible as a composable skill registry across MCP-compatible hosts without manual configuration.

Provides dynamic, context-aware code assistant skills through hybrid RAG (vector + knowledge graph), enabling runtime skill discovery, automatic toolchain-based recommendations, and on-demand loading from multiple git repositories.

Enables AI agents to keep skill catalogs outside the main LLM context and load only task-relevant SKILL.md instructions via a single MCP tool, reducing context overhead.

A remote MCP server that exposes a library of Claude-style SKILL.md packages, enabling Claude to search, retrieve skills, and read supporting files via MCP tools.

Remote MCP server for Claude that exposes a library of SKILL.md packages, enabling listing, searching, and retrieving skill instructions and supporting files for use in conversations.

An MCP server that serves company documentation from a Markdown folder, exposing read-only search and fetch tools for agents to discover and retrieve authoritative documents.

Enables external clients to query a vector knowledge base about skills and roles through an MCP retrieval tool, supporting RAG-based question answering with tool-calling.