Agent skill · data analytics · pedronauck
qmd
Search local markdown knowledge bases, notes, docs, and wikis with QMD. Use when users ask to find notes, retrieve documents, inspect a wiki, answer from indexed markdown, or set up QMD access.
Why this skill is useful
Provides specific query syntax and methods for searching markdown content that the AI wouldn't reliably generate on its own.
What it needs
Requires @tobilu/qmd installed locally. About 7k tokens when loaded. Last updated 2026-08-01. 564 stars on the source repository.
What this skill does
QMD - Query Markdown Documents How search works QMD searches local markdown collections: notes, docs, wikis, transcripts, and project knowledge bases. Use it before web search when the answer may already be in indexed local files. The workflow is always: 1. Search for candidate documents. 2. Retrieve the full source with qmd get or qmd multi-get. 3. Answer from retrieved text, citing paths or docids. Do not answer from snippets alone when the user needs facts, decisions, quotes, or nuance. Snippets are only leads. Typical loop: Default to structured qmd query with intent:, lex:, vec:, and hyde: fields that you write yourself. You are a better query expander than the built-in model: you know the user's actual goal, the domain vocabulary, and the nearby-but-wrong concepts to avoid. Do not just paste the user's words into qmd query "..." and hope the expansion model guesses right — supply the intent: and craft the lexical and semantic terms deliberately (see Pick the right search mode). When reporting what you retrieved, a compact note is enough; do not paste whole files unless needed: Pick the right search mode Use BM25 lexical search when you know exact words, titles, names, code symbols, or rare phrases: Use qmd query with structured fields when the user describes an idea indirectly, uses different wording than the source, or needs conceptual recall. This is the default mode — write the fields yourself rather than leaning on query expansion. Combine exact anchors with semantic recall: Structured query fields (you author each one — do not delegate this to the expansion model): intent: states what you are trying to find and what to avoid. Always supply this. It steers ranking away from nearby-but-wrong concepts. lex: exact terms, aliases, titles, code symbols, and rare words you expect in the source. This is your own keyword expansion. vec: paraphrases the idea in natural language, in source-like wording. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills pedronauck/qmd