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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).

jperrello 779de9b 4 files · 7.9 KB Updated
Coding & Dev Tools

File contents

style-corpus

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/).

jperrello/C0BALT_CUT/tree/main/.claude/skills/style-corpus commit 779de9b1cd

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npx skillmds@latest add jperrello/style-corpus