querying-markdown

querying-markdown

mq is "jq for Markdown" — it parses a .md file into a node stream and lets you select, filter, and transform by structure (.h2, .code("rust"), .link) instead of by line-matching. Reach for it when the task is structural: "every H2 title", "all bash code blocks", "a table of contents", "strip the frontmatter". For plain substring search, grep is still the right tool; for code (not prose) structure, use tree-sitting.

Before you use mq: is this actually a structural task?

mq parses the whole document into a node tree before it answers, and that parse cost is real (see Empirical findings). Most "query a markdown file" tasks don't need it. Decide first, using the target — not the file type:

If your task lands in a grep/awk row, do not install mq — close this skill and use the line tool. Diagnosed 2026-06-04: a full-KJV smoke test queried books/chapters/verses (all line-prefix structure) with mq — ~3.3 s per query where grep is milliseconds, the same answers, and a grep post-filter still needed on top. Wrong-shape corpus; mq's selectors earn their parse cost only on the structural rows.

The judgment call is whether the case is actually line-prefix or only looks it. A heading is a prefix; a heading you want demoted with its subtree, or a match you must re-emit as valid Markdown, is structure — mq's row even when the match looks like a prefix.

Setup

mq is a single static binary, not preinstalled. Install on first use (idempotent — exits early if already present, ~1s, no build step):

bash /mnt/skills/user/querying-markdown/scripts/install-mq.sh

This drops the pinned mq release into /usr/local/bin. Override the version with MQ_VERSION=vX.Y.Z.

Usage

mq 'QUERY' file.md          # query a file
cat file.md | mq 'QUERY'    # query stdin
mq repl                     # interactive REPL — use to test syntax fast

A node stream flows left→right through |. Selectors (.h, .code, .link) pick nodes; functions (to_text, slugify, map, len) transform them. self is the current node.

mq '.h2 | to_text()' README.md            # every H2 as plain text
mq '.code("python") | to_text()' file.md  # all python code blocks
mq '.h.level' file.md                     # heading depth per heading
mq -F json '.h2 | to_text()' file.md      # results as JSON
mq '.h2 | to_text()' file.md | wc -l      # count matches (reliable idiom)

Empirical findings

Measured 2026-06-04 against a full public-domain KJV Bible (66 files, 4.28 MB).

Parse-bound, not query-bound. mq reparses the whole document on every invocation; latency tracks document size, not selector or match count. On the 4.28 MB file every query — whether it returned 66 matches or 32,418 — ran ~3.2–3.3 s (~1.3 MB/s); on a normal-sized doc it is single-digit ms. Never loop mq per query over a large corpus: extract once with -F json and work on the result, or accept a constant per-call parse tax.

Selectors return nodes, not your domain concepts. .h2 over the KJV returned 1,250 nodes — 1,184 chapter headings plus 66 eof markers the source appended per file, while single-chapter books emitted no chapter heading at all. .text also pulled heading text into the paragraph stream. A raw selector count is a node count; map it to your concept with an explicit predicate (e.g. grep -E '^[0-9]+ ' for verses) and check it against a known total before trusting the number.

An empty result is ambiguous. Zero output means either the selector matched nothing or mq never ran — a wrapper like time/env failed in dash, or a malformed heredoc swallowed the command. Re-run the bare mq 'QUERY' file.md before concluding a selector or function is broken. (Self-inflicted 2026-06-04: a time: not found shell error read as a to_text() defect; to_text() on code blocks works.)

Reference

Selector aliases, the built-in function library, table-of-contents and transform recipes, in-place-edit caveats, and CLI flags live in references/cheatsheet.md. Read it before writing a non-trivial query — the dialect is jq-like, not jq, so the function names differ. When unsure of syntax, mq repl gives instant feedback.