# Match Structure > Paragraph and sentence-level style metrics for a research corpus. Computes quantitative profiles (sentence/paragraph distributions, burstiness as coefficient of variation of sentence length, passive voice, hedging, word/phrase/idiom frequencies, per-section metrics), corpus aggregation, term over/underuse comparison, and a plagiarism similarity guard (n-gram shingling with baseline exclusion). Provides voice anchor retrieval from writing-voice exemplars via tf-idf. The section-level driver (compare, blueprint, rewrite) moved to match-outline. Triggers: style profile, quantitative metrics, sentence stats, frequency tables, plagiarism check, similarity check, voice anchors, corpus profile, word frequency, burstiness, sentence length variance. - Skill: `petar-djukic/match-structure` (Agent Skill, multi-file: 12 files) - Install (CLI): `npx skillmds@latest add petar-djukic/match-structure` - Raw SKILL.md: https://api.skillmd.com/api/skills/petar-djukic/match-structure/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Docs & Writing - Author: petar-djukic (https://skillmd.com/u/petar-djukic) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/petar-djukic/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:** `/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:** `/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/.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 ```bash RUN="pixi run --manifest-path /../../pixi.toml python" ``` ## style.py subcommands ```bash $RUN /scripts/style.py --db profile # one paper, full JSON $RUN /scripts/style.py --db corpus # aggregate, write voice-profile.json $RUN /scripts/style.py --db compare # metric deltas vs corpus $RUN /scripts/style.py freq # frequency tables only $RUN /scripts/style.py similarity --against [--baseline ] $RUN /scripts/style.py burstiness [--baseline ] [--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 ` 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 ```bash $RUN /scripts/voice_anchors.py discover $RUN /scripts/voice_anchors.py profile [--voice-dir D | --for file] [--force] $RUN /scripts/voice_anchors.py anchors --text |- [--for file] [-k N] [--role R] ``` ## venue_profile.py subcommands ```bash $RUN /scripts/venue_profile.py discover # find venues/ via walk-up $RUN /scripts/venue_profile.py list --for # available venue names $RUN /scripts/venue_profile.py show --venue N --for # validated profile JSON $RUN /scripts/venue_profile.py validate $RUN /scripts/venue_profile.py bootstrap --voice-dir D --tags a,b [--role R] [--stratum pre-ai] $RUN /scripts/venue_profile.py bootstrap --voice-dir D --tags a,b --venue N --write $RUN /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/.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=`); 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.