Index local Markdown notes with BM25 keyword search, vector semantic search, and LLM reranking.
npx skills add https://github.com/gabrielkoerich/skills --skill qmd-gabrielkoerich
Please help me install this Agent Skill. Skill: qmd Source: https://github.com/gabrielkoerich/skills/tree/main/qmd Command: npx skills add https://github.com/gabrielkoerich/skills --skill qmd-gabrielkoerich
Index Markdown notes locally to enable fast retrieval.
Install qmd and index your Markdown notes to enable instant local search.
High-intent search queries and answers about installing and using this skill.
You can search markdown notes locally without API keys by indexing them offline with embeddable models. This enables fast retrieval using BM25 keyword search and vector semantic search entirely on local resources.
Combining BM25 keyword search with vector semantic search and LLM reranking improves result relevance across large markdown collections. This hybrid approach ensures both exact term matching and contextual understanding during retrieval.
Yes, you can use LLM reranking for large markdown collections offline. The indexing and reranking processes operate entirely on local resources, utilizing embeddable models without needing external API keys.
To index markdown notes for fast local retrieval, you process the files locally to build a searchable index. This index then supports instant BM25 keyword searches and vector semantic queries over your notes.
Local vector search works for markdown notes without cloud APIs by using embeddable models for offline indexing. This allows you to perform semantic searches and LLM reranking completely on local resources.
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