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BytesAgain β€Ί Skills β€Ί QMD Search
πŸ¦€ ClawHub

QMD Search

by @anshumanbh

Search markdown knowledge bases efficiently using qmd. Use this when searching Obsidian vaults or markdown collections to find relevant content with minimal token usage.

Versionv1.0.0
Downloads2,749
Installs3
#clawhub
TERMINAL
clawhub install anshumanbh-qmd

πŸ“– About This Skill

name: qmd description: Search markdown knowledge bases efficiently using qmd. Use this when searching Obsidian vaults or markdown collections to find relevant content with minimal token usage. argument-hint: " [--collection ] [--semantic]"

QMD Search Skill

Search markdown knowledge bases efficiently using qmd, a local indexing tool that uses BM25 + vector embeddings to return only relevant snippets instead of full files.

Why Use This

  • 96% token reduction - Returns relevant snippets instead of reading entire files
  • Instant results - Pre-indexed content means fast searches
  • Local & private - All indexing and search happens locally
  • Hybrid search - BM25 for keyword matching, vector search for semantic similarity

    • Commands

    Search (BM25 keyword matching)

    qmd search "your query" --collection
    Fast, accurate keyword-based search. Best for specific terms or phrases.

    Vector Search (semantic)

    qmd vsearch "your query" --collection
    Semantic similarity search. Best for conceptual queries where exact words may vary.

    Hybrid Search (both + reranking)

    qmd hybrid "your query" --collection
    Combines both approaches with LLM reranking. Most thorough but often overkill.

    How to Use

    1. Check if collection exists: 

       qmd collection list

    2. Search the collection: 

       # For specific terms
   qmd search "api authentication" --collection notes
       # For conceptual queries
   qmd vsearch "how to handle errors gracefully" --collection notes

    3. Read results: qmd returns relevant snippets with file paths and context

    Setup (if qmd not installed)

    # Install qmd
bun install -g https://github.com/tobi/qmd
    Add a collection (e.g., Obsidian vault)
    
qmd collection add ~/path/to/vault --name notes
    Generate embeddings for vector search
    
qmd embed --collection notes

    Invocation Examples

    /qmd api authentication          # BM25 search for "api authentication"
/qmd how to handle errors --semantic   # Vector search for conceptual query
/qmd --setup                     # Guide through initial setup

    Best Practices

  • Use BM25 search (qmd search) for specific terms, names, or technical keywords
  • Use vector search (qmd vsearch) when looking for concepts where wording may vary
  • Avoid hybrid search unless you need maximum recall - it's slower
  • Re-run qmd embed after adding significant new content to keep vectors current

    • Handling Arguments

  • $ARGUMENTS contains the full search query
  • If --semantic flag is present, use qmd vsearch instead of qmd search
  • If --setup flag is present, guide user through installation and collection setup
  • If --collection is specified, use that collection; otherwise default to checking available collections

    • Workflow

    1. Parse arguments from $ARGUMENTS 2. Check if qmd is installed (which qmd) 3. If not installed, offer to guide setup 4. If searching: - List collections if none specified - Run appropriate search command - Present results to user with file paths 5. If user wants to read a specific result, use the Read tool on the file path

    πŸ“‹ Tips & Best Practices

  • Use BM25 search (qmd search) for specific terms, names, or technical keywords
  • Use vector search (qmd vsearch) when looking for concepts where wording may vary
  • Avoid hybrid search unless you need maximum recall - it's slower
  • Re-run qmd embed after adding significant new content to keep vectors current