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 skills add https://github.com/oaustegard/claude-skills --skill 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.
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.
Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.
Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats.
Fast CLI/Python queries to 20+ bioinformatics databases. Use for quick lookups: gene info, BLAST searches, AlphaFold structures, enrichment analysis. Best for interactive exploration, simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices.
Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot/TrEMBL. For Python workflows with multiple databases, prefer bioservices (unified interface to 40+ services). Use this for direct HTTP/REST work or UniProt-specific control.
Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot/TrEMBL. For Python workflows with multiple databases, prefer bioservices (unified interface to 40+ services). Use this for direct HTTP/REST work or UniProt-specific control.
BullMQ expert for Redis-backed job queues, background processing, and reliable async execution in Node.js/TypeScript applications. Use when: bullmq, bull queue, redis queue, background job, job queue.
Create custom external web service APIs for Moodle LMS. Use when implementing web services for course management, user tracking, quiz operations, or custom plugin functionality. Covers parameter validation, database operations, error handling, service registration, and Moodle coding standards.
Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats.
Take oaustegard/querying-markdown from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.