qmd – OpenClaw Skill

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name: qmd description: Fast local search for markdown files, notes, and docs using qmd CLI. Use instead of find for file discovery. Combines BM25 full-text search, vector semantic search, and LLM reranking—all running locally. Use when searching for files, finding code, locating documentation, or discovering content in indexed collections.

qmd — Fast Local Markdown Search

When to Use

• Finding files — use instead of find across large directories (avoids hangs)
• Searching notes/docs — semantic or keyword search in indexed collections
• Code discovery — find implementations, configs, or patterns
• Context gathering — pull relevant snippets before answering questions

Quick Reference

Search (most common)

# Keyword search (BM25)
qmd search "alpaca API" -c projects

# Semantic search (understands meaning)
qmd vsearch "how to implement stop loss"

# Combined search with reranking (best quality)
qmd query "trading rules for breakouts"

# File paths only (fast discovery)
qmd search "config" --files -c kell

# Full document content
qmd search "pattern detection" --full --line-numbers

Collections

# List collections
qmd collection list

# Add new collection
qmd collection add /path/to/folder --name myproject --mask "*.md,*.py"

# Re-index after changes
qmd update

Get Files

# Get full file
qmd get myproject/README.md

# Get specific lines
qmd get myproject/config.py:50 -l 30

# Get multiple files by glob
qmd multi-get "*.yaml" -l 50 --max-bytes 10240

Output Formats

• --files — paths + scores (for file discovery)
• --json — structured with snippets
• --md — markdown formatted
• -n 10 — limit results

Tips

1. Always use collections (-c name) to scope searches
2. Run qmd update after adding new files
3. Use qmd embed to enable vector search (one-time, takes a few minutes)
4. Prefer qmd search --files over find for large directories

Models (auto-downloaded)

• Embedding: embeddinggemma-300M
• Reranking: qwen3-reranker-0.6b
• Generation: Qwen3-0.6B

All run locally — no API keys needed.