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.

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Install skill "qmd" with this command: npx skills add montyanderson/qmd-skill/montyanderson-qmd-skill-qmd

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

  1. Search to find relevant documents:

    qmd query "quarterly planning" -n 5 --json
    
  2. Review search results (docids, scores, snippets)

  3. Retrieve full content of relevant documents:

    qmd get "#abc123" --json
    
  4. Synthesize information from retrieved documents to answer the user's question

Common Options

OptionDescription
-n <num>Number of results (default: 5)
--jsonJSON output for parsing
--filesTSV 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 operation
  • qmd 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 RangeMeaning
0.8 - 1.0Highly relevant
0.5 - 0.8Moderately relevant
0.2 - 0.5Somewhat relevant
0.0 - 0.2Low relevance

Resources

references/

  • cli_reference.md - Complete CLI API documentation with all commands, options, and examples

Source Transparency

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General

qmd

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