Replace grep-based memory search with hybrid BM25 and vector semantic search.
npx skills add https://github.com/iia-arg/claudeclaw --skill add-qmd-iia-arg
Please help me install this Agent Skill. Skill: add-qmd Source: https://github.com/iia-arg/claudeclaw/tree/main/skills/add-qmd Command: npx skills add https://github.com/iia-arg/claudeclaw --skill add-qmd-iia-arg
This Skill improves memory retrieval by replacing basic keyword searches with a hybrid semantic approach, increasing recall accuracy and relevance.
Add this skill to upgrade your existing memory search capabilities with local, semantic indexing using QMD.
High-intent search queries and answers about installing and using this skill.
You can improve local markdown memory search accuracy by replacing basic grep with a hybrid BM25 and vector semantic search system, which increases recall relevance for large repositories.
The best way to perform semantic search across large markdown notes is using a local hybrid retrieval system that combines BM25 keyword matching with vector search and LLM re-ranking.
Yes, you need the QMD dependency to implement local semantic indexing, as it provides the core BM25 and vector search integration required for fast document recall.
Yes, you can replace grep-based retrieval in structured agent workflows by integrating a local hybrid indexing system that uses vector search and LLM re-ranking for smarter document recall.
Traditional keyword search misses relevant context because it lacks semantic understanding, whereas a hybrid BM25 and vector approach captures deeper meaning for more accurate memory retrieval.
ramonclaudioClone a GitHub repository into ~/Developer/refs/ and index it with QMD.
ourmemSearch the ourmem persistent memory store with natural language queries.
JSai23Enable semantic search of local vault notes using qmd with BM25, vector search, and LLM re-ranking.
RheasilviaIndex local markdown notes, transcripts, and documentation for hybrid search.
