qmd

Search an Obsidian vault with BM25, vector, and reranked hybrid queries.

Updated Jan 3, 2026
One-click install
npx skills add https://github.com/timkrase/clawdis-skills --skill qmd-timkrase
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: qmd
Source: https://github.com/timkrase/clawdis-skills/tree/main/qmd
Command: npx skills add https://github.com/timkrase/clawdis-skills --skill qmd-timkrase

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill allows users to efficiently search through Tim's personal knowledge base (Obsidian vault) using advanced search techniques, including keyword, semantic, and hybrid approaches.

Core Features & Use Cases

  • Semantic Search: Understands the meaning behind queries to find relevant notes even if exact keywords aren't used.
  • Keyword Search: Provides fast and accurate results for specific terms.
  • Hybrid Search: Combines keyword and semantic search for the best quality results.
  • Use Case: When Tim needs to recall information about a past project, a personal preference, or any topic he's documented, this Skill can quickly surface the relevant notes.

Quick Start

Use the qmd skill to search for "quarterly planning" in the vault.

Frequently Asked Questions about qmd

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I search an Obsidian vault using semantic search?

Semantic search of an Obsidian vault uses vector embeddings to match the meaning of your query, finding relevant notes even without exact keywords. This Skill combines vector search with BM25 keyword retrieval and LLM reranking for hybrid results.

What is hybrid search for personal knowledge bases?

Hybrid search for personal knowledge bases combines BM25 keyword search with vector semantic search and LLM reranking. This approach surfaces relevant notes, past decisions, and documentation by matching both exact terms and contextual meaning.

Do I need a specific binary to search notes with AI?

Yes, searching notes with AI requires the 'qmd' binary and an indexed Obsidian vault collection. The Skill depends on these components to execute keyword, semantic, and hybrid search queries against your personal documentation.

Can I find past project decisions in my notes without exact keywords?

Finding past project decisions without exact keywords is possible through vector semantic search. The Skill interprets the meaning behind your query to locate relevant notes and documentation within your Obsidian vault.

What is the best way to recall personal preferences documented in Obsidian?

The best way to recall personal preferences documented in Obsidian is using hybrid search, which combines fast BM25 keyword matching with vector semantic search and LLM reranking to surface accurate notes.

Why does semantic search require an indexed vault collection?

Semantic search requires an indexed vault collection because the 'qmd' binary must process and store vector embeddings of your notes beforehand. Without this index, the LLM reranking and vector retrieval cannot execute.