git:20260728.c30c7bf to git:20260729.2fb08cb

---

name: match-structure

description: >-

Paragraph and sentence-level style metrics for a research corpus.

Computes quantitative profiles (sentence/paragraph distributions,

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.

---

*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`.

## Running the scripts

```bash

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

```

## style.py subcommands

```bash

$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>]

```

## voice_anchors.py subcommands

```bash

$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]

```

## 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.