Index local documents and search them with BM25 and vector embeddings.
npx skills add https://github.com/drshailesh88/Krypto --skill qmd-drshailesh88
Please help me install this Agent Skill. Skill: qmd Source: https://github.com/drshailesh88/Krypto/tree/main/skills/qmd Command: npx skills add https://github.com/drshailesh88/Krypto --skill qmd-drshailesh88
Index and search local documents efficiently by indexing files and providing search over them using a hybrid of BM25 lexical scoring and vector similarity with optional reranking.
Install qmd (Node) and start indexing:
High-intent search queries and answers about installing and using this skill.
You can search local documents by indexing files and querying them with a hybrid of BM25 lexical scoring and vector similarity. This approach blends keyword matching with semantic retrieval to locate relevant files in project collections.
The best way to index a local code repository is using a CLI tool to scan files with specific masks. You add a collection pointing to your directory, update the index, and run keyword or vector searches to quickly locate relevant files.
Yes, you need Node installed because the CLI is installed via npm. Once installed globally, you can use it to index local documents, notes, and code repositories for hybrid search operations.
Yes, the CLI supports MCP mode to blend lexical and semantic results. This allows you to combine BM25 lexical ranking with vector similarity and optional reranking for improved retrieval accuracy.
Hybrid search with reranking improves personal knowledge base retrieval by combining BM25 keyword scoring with vector similarity. Optional reranking refines these blended results, ensuring fast and highly relevant document recall.
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