The QMD skill empowers Claude to efficiently navigate and query local file systems by leveraging advanced indexing techniques including BM25, vector embeddings, and reranking. By integrating with Ollama for local embeddings, it allows users to perform high-precision semantic searches across codebases or documentation without sending data to external search providers. This skill is ideal for developers managing large-scale knowledge bases or private projects where traditional keyword search is insufficient and data privacy is paramount.
主な機能
01Model Context Protocol (MCP) support for standardized tool interoperability
02Granular document retrieval with support for specific line ranges and contexts
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04Hybrid search combining BM25 keyword matching and vector-based semantic retrieval
05Seamless integration with Ollama for local embedding and reranking models
06Local indexing and storage for a privacy-first document management experience
ユースケース
01Navigating large technical documentation repositories to find specific implementation patterns
02Quickly locating relevant code snippets or context across multiple disparate project directories
03Building a local Retrieval-Augmented Generation (RAG) system for private codebases