Match Structure

Split from the original match-structure (GH-291, 2026-07): the section-level driver (compare, blueprint extraction, section-by-section rewrite) moved to match-outline. This skill retains the quantitative metrics engine, corpus aggregation, frequency analysis, and the similarity guard that other prose skills import.

Match structure (paragraph/sentence-level metrics)

This skill provides the quantitative style measurement layer that other prose skills depend on. It profiles markdown papers at the sentence and paragraph level — distributions, passive voice, hedging, citation density, word and phrase frequencies, stock idiom usage — and aggregates those into a corpus profile. It also provides the similarity plagiarism guard used by match-outline's rewrite mode and match-voice's verify step.

Where things live

• Corpus: <db-dir>/papers/*.md — the markdown conversions fetched by update-references, selected via entries in references.yaml. Default selection is entries with status: summarized; pass --all to style.py corpus to include every entry with an md_path.
• Quantitative profile: <db-dir>/voice-profile.json, written by style.py corpus. Regenerate only when the corpus changes.
• Voice anchors: passage-level tf-idf retrieval from writing-voice/ exemplars, via voice_anchors.py.
• Venue profiles: writing-voice/venues/<name>.yaml — per-venue parameter bundles (anchor query, blueprint, targets, tell lexicon, gates) consumed by humanize/filter-tells/tighten-style. Schema in the writing-voice repository rule; loader/validator is venue_profile.py.

Running the scripts

RUN="pixi run --manifest-path <skill>/../../pixi.toml python"

style.py subcommands

$RUN <skill>/scripts/style.py --db <db-path> profile <paper.md>   # one paper, full JSON
$RUN <skill>/scripts/style.py --db <db-path> corpus                # aggregate, write voice-profile.json
$RUN <skill>/scripts/style.py --db <db-path> compare <draft.md>    # metric deltas vs corpus
$RUN <skill>/scripts/style.py freq <paper.md>                      # frequency tables only
$RUN <skill>/scripts/style.py similarity <file> --against <sources> [--baseline <draft>]
$RUN <skill>/scripts/style.py burstiness <draft.md> [--baseline <before.md>] [--per-paragraph] [--text]

burstiness — sentence-length dispersion

profile already carries every burstiness field; burstiness is the compact view of the same numbers, for the places a report wants one line rather than a page of JSON. It prints sentence count, mean, stdev, CV, min, max, median, p10, p90, and a word-length histogram.

CV — stdev over mean — is the scale-free form, and the one to quote. Stdev alone conflates dispersion with register: a paper averaging 28-word sentences carries a larger stdev than a newsletter averaging 14 without being any less uniform. The percentiles and the histogram say where the variance sits, which is what a rewrite pass needs, since a document can hit a CV target by growing one 60-word sentence and that is not the same prose as one that alternates.

--baseline <before.md> adds a delta block over every scalar, which is the before/after column in the humanize report. --per-paragraph adds a row per multi-sentence paragraph so a rewrite can find the flat stretches instead of reshaping prose that already varies. --text renders one line per document instead of JSON.

Which fields aggregate: sentence_length_cv, _median, _p10, and _p90 are in METRIC_KEYS, so they appear in corpus profiles, compare deltas, and venue targets. Min and max are not — a sample's extrema move with sample size rather than with style, and averaging them across a corpus produces a number that means nothing. The histogram is a dict and does not average at all.

filter-tells reports its own sentence_length_cv in the structural scan. It runs slightly lower than this one, because that script drops sentence fragments under four words and those are exactly the ones that widen the spread. For a before/after comparison, keep both sides on one tool.

voice_anchors.py subcommands

$RUN <skill>/scripts/voice_anchors.py discover <file>
$RUN <skill>/scripts/voice_anchors.py profile [--voice-dir D | --for file] [--force]
$RUN <skill>/scripts/voice_anchors.py anchors --text <file>|- [--for file] [-k N] [--role R]

venue_profile.py subcommands

$RUN <skill>/scripts/venue_profile.py discover <file>            # find venues/ via walk-up
$RUN <skill>/scripts/venue_profile.py list --for <file>          # available venue names
$RUN <skill>/scripts/venue_profile.py show --venue N --for <file> # validated profile JSON
$RUN <skill>/scripts/venue_profile.py validate <profile.yaml>
$RUN <skill>/scripts/venue_profile.py bootstrap --voice-dir D --tags a,b [--role R] [--stratum pre-ai]
$RUN <skill>/scripts/venue_profile.py bootstrap --voice-dir D --tags a,b --venue N --write
$RUN <skill>/scripts/venue_profile.py set-anchors --venue N --voice-dir D --arm "tags~clipped" [--composite X]

Python consumers import venue_profile.resolve(start_path=..., venue=...), which raises on schema errors — a broken profile is refused, never partially applied.

bootstrap (GH-339) measures a corpus slice — a manifest query (role/tags/stratum) or an explicit --files list — with the same metric engine as style.py corpus, and emits the profile's targets/targets_std/targets_provenance block; --write merges it into an existing venues/<name>.yaml (author the profile first). Profiles are measured, never hand-written. set-anchors records an anchor query into a profile with provenance — from tune-anchors arm expressions (repeatable --arm, parsed by tune-anchors' own parser so the syntax cannot drift) or from explicit --role/--tags/--stratum/--author flags.

Consumers

• match-outline — imports style for corpus selection, the similarity guard, and the CITATION_RE pattern. Uses gpt-oss:120b-cloud via Ollama for whole-document structural rewriting.
• match-voice — imports style.similarity_report for the rewrite verification gate; imports voice_anchors for anchor retrieval.
• filter-tells — consumes voice-profile.json as a detector input (--voice-profile=<path>); uses voice_anchors.py for anchor retrieval and baseline profiling.
• tighten-style — imports style for nominalization density metrics.
• tune-anchors — imports voice_anchors for the anchor tuning workflow.

Dependencies

style.py is stdlib-only except PyYAML. voice_anchors.py uses PyYAML. No model calls — this is the deterministic half of the analysis pipeline.