Agent skill
qmd
Search and retrieve documents from local markdown knowledge bases using the qmd CLI (Quick Markdown Search). This skill should be used when searching notes, documentation, meeting transcripts, or any indexed markdown content. Triggers on requests like "search my notes for...", "find in my knowledge base...", "what do my notes say about...", or any query that needs to search local markdown files.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/qmd-neversight-skills-feed
SKILL.md
QMD - Quick Markdown Search
Overview
QMD is an on-device search engine for markdown-based knowledge bases. It provides three search modes with increasing quality/latency tradeoffs, plus document retrieval capabilities. All processing runs locally using embedded GGUF models.
Search Modes
Choose the appropriate search mode based on the query type:
1. BM25 Search (qmd search) - Fast Keyword Search
qmd search "<query>" -n 10 --json
When to use:
- Exact keyword or phrase searches
- Technical terms, function names, specific identifiers
- When speed matters more than semantic understanding
2. Vector Search (qmd vsearch) - Semantic Search
qmd vsearch "<query>" -n 10 --json
When to use:
- Conceptual queries ("how do I...", "what about...")
- When exact keywords are unknown
- Finding related content with different wording
3. Hybrid Search (qmd query) - Highest Quality
qmd query "<query>" -n 10 --json
When to use:
- Important searches where accuracy matters
- Complex queries requiring understanding
- Default choice when unsure which mode to use
Document Retrieval
After searching, retrieve full document content:
# By file path
qmd get "collection/path/to/file.md" --json
# By document ID (from search results)
qmd get "#a1b2c3" --json
# Multiple documents
qmd multi-get "collection/*.md" -l 500 --json
Typical Workflow
-
Search to find relevant documents:
bashqmd query "quarterly planning" -n 5 --json -
Review search results (docids, scores, snippets)
-
Retrieve full content of relevant documents:
bashqmd get "#abc123" --json -
Synthesize information from retrieved documents to answer the user's question
Common Options
| Option | Description |
|---|---|
-n <num> |
Number of results (default: 5) |
--json |
JSON output for parsing |
--files |
TSV format: docid, score, filepath |
-c <name> |
Restrict to specific collection |
--min-score <n> |
Minimum relevance threshold (0-1) |
-l <lines> |
Max lines per document |
Index Status
Check available collections and index health:
qmd status
Critical Constraints
Never run these commands automatically:
qmd collection add- Expensive indexing operationqmd embed- Generates embeddings (slow, resource-intensive)qmd update- Re-indexes all collections
These operations should only be run when explicitly requested by the user.
Score Interpretation
| Score Range | Meaning |
|---|---|
| 0.8 - 1.0 | Highly relevant |
| 0.5 - 0.8 | Moderately relevant |
| 0.2 - 0.5 | Somewhat relevant |
| 0.0 - 0.2 | Low relevance |
Resources
references/
cli_reference.md- Complete CLI API documentation with all commands, options, and examples
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