querying-markdown/SKILL.md
Query, filter, and transform Markdown structurally with mq — a jq-like CLI for Markdown. Use to extract headings/sections/code-blocks/links from .md files, build a table of contents, pull code blocks of a given language, slice or reshape LLM prompt/output Markdown, or batch-transform docs. Triggers on "extract sections from this markdown", "get all the code blocks", "jq for markdown", "mq", or any structural query over Markdown that grep/Read can't do cleanly.
npx skillsauth add oaustegard/claude-skills querying-markdownInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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.
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:
| Your target | Use | Why |
| --- | --- | --- |
| Lines with a fixed prefix — #/## headings, > quotes, - bullets, a leading line/verse number | grep / awk | Line-matching, not structure. grep is faster and already installed. |
| A substring anywhere | grep | mq adds nothing. |
| Code structure inside fences (ASTs, symbols, call sites) | tree-sitting | mq sees the fence, not the code inside it. |
| Language-filtered code blocks (.code("bash")); links as structured (text, url) (-F json '.link') | mq | grep can't filter a fenced block by language without a brittle hand-rolled fence state machine. |
| Markdown→Markdown transforms that must emit valid Markdown — demote/promote headings, rebuild a TOC with anchors, in-place edit | mq | sed doesn't know structure and will corrupt nesting/fences. |
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.
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.
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)
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.)
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.
development
Write effective instructions for Claude: project instructions, standalone prompts, and skill content. Use when users need help writing prompts, setting up project instructions, choosing between instruction formats, or improving how they communicate with Claude. Covers writing principles, model-aware calibration, and format selection. For building and testing complete skills, use skill-creator instead.
data-ai
Discover and load skills on demand from /mnt/skills/user/. Use when you need a capability but don't know which skill provides it, when the boot-emitted skill list is names-only and you need a full description, or when you want to list the catalog. Verbs are list (names only), search (rank by name/description match against a query), and show (emit the full SKILL.md for a named skill).
documentation
Reads the visual content of slides, pages, and images the way a human would, not just their embedded text. Use when a PPTX or PDF has image slides, screenshots, charts, scanned figures, or flattened-to-image layouts that the built-in pptx/pdf skills read as empty; when asked to transcribe, describe, OCR, or extract what is shown in an image, slide deck, or document page; or when embedded-text extraction returned little or nothing from a visually rich file. Triggers on 'read this deck', 'what's on these slides', 'transcribe', 'OCR', 'extract text from image', 'describe this chart/diagram', .pptx/.pdf/.png/.jpg with visual content.
development
Portrait Mode for SVGs — foveated vectorization with 4-zone selective detail. Combines vision annotations, MediaPipe segmentation/landmarks, and optional saliency. Like phone portrait mode, but vectorized. Use when vectorizing a portrait or photo where subject detail should outrank background detail.