# Style Corpus

> The offline LEARN phase of the style-replication loop. `learn <url|file>...` downloads each Group C exemplar short (yt-dlp, media into gitignored work/_style/<id>/), profiles it sidecar-free via profile-clip (vision ON), and stores the small JSON Style Profile in references/profiles/ (checked in); `distill` aggregates every profile into references/targets.json — per-field robust distribution targets (median + MAD-derived sigma, floored at 10% of the median) with each levered field carrying its code lever, so the style-gate can compare our span 0 against a DISTRIBUTION of good shorts instead of overfitting one exemplar; `show` prints the targets. Deterministic distill, non-fatal per exemplar, idempotent (profile-clip's .ppmeta makes re-learn a no-op on unchanged media).

- Skill: `jperrello/style-corpus` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add jperrello/style-corpus`
- Raw SKILL.md: https://api.skillmd.com/api/skills/jperrello/style-corpus/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: jperrello (https://skillmd.com/u/jperrello)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/jperrello/style-corpus

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# style-corpus

```bash
bash .claude/skills/style-corpus/style-corpus.sh learn <urlC1> <urlC2> ...
bash .claude/skills/style-corpus/style-corpus.sh distill
bash .claude/skills/style-corpus/style-corpus.sh show
```

`references/targets.json`: `{style_profile_version, n, clips[], fields:{"cuts.cuts_per_min": {median, sigma, n, lever, invert} | {median, sigma, n, diagnostic:true}}}`.
Only levered fields participate in the style-gate match; diagnostic fields are
reported but can never fail the gate (the unreachable-target protection —
production facts like multicam/staged are measured, not chased).

The gate self-arms: `style-gate` no-ops until `targets.json` exists with
`n >= SG_MIN_REFS` (default 3). Env: `STYLE_REFS` (corpus dir, default
`references/`).