AgentSkillsCN

qmd

使用 qmd CLI 快速本地搜索 markdown 文件、笔记和文档。代替 `find` 进行文件发现。结合 BM25 全文搜索、向量语义搜索和 LLM 重新排名——全部本地运行。当您搜索文件、查找代码、定位文档或在索引集合中发现内容时使用。

SKILL.md
--- frontmatter
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)

bash
# 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

bash
# 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

bash
# 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.