According to AlphaMoat, as of 2026-09, qmd Search has 4,772 downloads and 2 stars — ranked #1,981 of 5,078 AI skills overall, and #185 of 442 in Knowledge.

Installs

333

Downloads

4.8K

Stars

Overall Rank

#1,981

Top 40% overall · Top 42% in Knowledge

Platform	Installs	Downloads	Stars	Comments
ClawHub ↗	152	4.6K	2	-
SkillHub ↗	333	4.8K	2	—

Platforms count differently and update at different times.

What this skill does

The qmd CLI offers keyword, semantic and reranked search, snippets, paths and full-file retrieval. Select intended folders as collections and update the index after changes. Vector search needs embedding setup and model downloads, so review scope, storage and compute before enabling it.

Profile published 2026-10-27

What you can do with it

Find relevant local documentation

When to use it

When indexed notes or source collections need searching

Example prompts

Generated from the skill’s documentation as examples; not results of actual runs.

Search only the agreed collection and show relevant snippets with file paths.

Monthly Trend

Downloads4.8K

Stars2

The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.

Month	Downloads	MoM	Stars
2026-09	4.8K	—	2
2026-08	4.3K	+3.1%	2
2026-07	4.2K	+6.2%	2
2026-06	4.0K	+3.2%	2
2026-05	3.8K	—	2
2026-03	3.3K	—	1

SKILL.md (original)

The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.

2.1 KB · ClawHub v1.1.0

---
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)

Show all (83 lines)

- **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.