Indexes local files and documentation to enable high-performance hybrid search via BM25 and vector embeddings.
QMD is a specialized local search engine designed to integrate with Claude Code via MCP, allowing for lightning-fast retrieval of information from local file systems. It combines traditional BM25 keyword search with modern vector-based semantic search and reranking, powered by Ollama, to provide highly relevant context to your AI assistant. This skill is particularly useful for developers managing large internal documentation sets or codebases who need immediate, context-aware answers without leaving their CLI environment.
Key Features
01Integration with Ollama for local embeddings and result reranking
02MCP (Model Context Protocol) support for seamless integration with AI assistants
03CLI-based document retrieval and management for local knowledge bases
04Hybrid search combining BM25 keyword matching and vector embeddings
05Local indexing of files and documentation with customizable masks
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Use Cases
01Providing semantic context to Claude from a private knowledge base using MCP
02Quickly locating and reading specific snippets from indexed files via CLI commands
03Searching through large local documentation folders for specific technical details
