安全扫描（ClawHub）

• VirusTotal ：可疑
• OpenClaw ：良性

OpenClaw 评估

The skill is an instruction-only wrapper for the local qmd tool and its requirements and instructions are coherent with its stated purpose of searching local Markdown collections.

安装（复制给龙虾 AI）

将下方整段复制到龙虾中文库对话中，由龙虾按 SKILL.md 完成安装。

请把本段交给龙虾中文库（龙虾 AI）执行：为本机安装 OpenClaw 技能「QMD Search」。简介：Search markdown knowledge bases efficiently using qmd. Use this when searching …。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装：https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/anshumanbh/anshumanbh-qmd/SKILL.md
（来源：yingzhi8.cn 技能库）

SKILL.md

打开原始 SKILL.md（GitHub raw）

---
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: "<search query> [--collection <name>] [--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)
```bash
qmd search "your query" --collection <name>
```
Fast, accurate keyword-based search. Best for specific terms or phrases.

### Vector Search (semantic)
```bash
qmd vsearch "your query" --collection <name>
```
Semantic similarity search. Best for conceptual queries where exact words may vary.

### Hybrid Search (both + reranking)
```bash
qmd hybrid "your query" --collection <name>
```
Combines both approaches with LLM reranking. Most thorough but often overkill.

## How to Use

1. **Check if collection exists**:
   ```bash
   qmd collection list
   ```

2. **Search the collection**:
   ```bash
   # 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)

```bash
# 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 <name>` 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