Index, embed, and semantically search local Markdown documents with the qmd CLI.
npx skills add https://github.com/pascalandy/dotfiles --skill qmd-pascalandy
Please help me install this Agent Skill. Skill: qmd Source: https://github.com/pascalandy/dotfiles/tree/main/dot_config/opencode/skill/qmd Command: npx skills add https://github.com/pascalandy/dotfiles --skill qmd-pascalandy
This Skill streamlines the indexing, embedding, and semantic search of markdown knowledge bases on local machines, enabling fast retrieval and exploration without external services.
Use the qmd skill to index the notes in the docs/ folder and perform a local semantic search for "project roadmap".
High-intent search queries and answers about installing and using this skill.
You can perform semantic search on local markdown documents by using a CLI to index files, generate embeddings, and query the resulting local vector store for context-aware retrieval.
The best way to index a personal knowledge base for fast retrieval is generating vector representations of markdown documents, allowing quick similarity searches across your local notes.
Yes, you need the qmd CLI installed in your environment along with access to your markdown files to index, update embeddings, and execute semantic searches locally.
Yes, you can search project documentation using context-aware queries by indexing the markdown files into a vector store and running semantic searches against the generated embeddings.
No, local indexing of markdown notes does not require external services. Embeddings and vector representations are created locally to enable fast retrieval and exploration on your machine.
johncarpenterSearch, retrieve, and index local Markdown files using the QMD CLI.
harperreedStore and retrieve user context via embedding-based semantic similarity search.
bahayonghangIndex Markdown notes into a local SQLite vector/full-text index.
JuanMS20Index Markdown and text files for local hybrid search with BM25, vectors, and reranking.
