What problem does it solve? Finding information across large collections of personal notes, meeting transcripts, and documentation is slow with plain grep and inaccurate with keyword-only search. This Skill sets up and operates qmd, a fully local search engine that combines keyword matching, semantic vector search, and LLM reranking so you can query your knowledge base in natural language without sending data to the cloud. ## Core Features & Use Cases - Three Search Modes: Run instant BM25 keyword search, semantic vector search, or full hybrid search with query expansion and LLM reranking depending on speed and quality needs. - Collection Management: Add directories as named collections, attach context descriptions to improve retrieval accuracy, and generate embeddings with a single command. - MCP Integration: Expose search tools directly to Hermes Agent via stdio or a persistent HTTP daemon that keeps models warm for fast repeated queries. - Use Case: You have years of markdown meeting notes and project docs. After indexing them with qmd, you ask "what decisions were made about the database migration" and get reranked, contextually relevant excerpts in seconds. ## Quick Start Use the qmd skill to search my notes collection for anything about the API redesign decisions.
