/extract-source-sample

name: extract-source-sample
description: Given the path to a finished content-goose ad-run folder, extract everything that defines that ad — recipe shot list, VO script, characters, voices, world, atom-skills, master mp4 — and emit a `source-sample.json` in the exact shape the `upload-ad-sample` skill writes to the Goose Ads library. Also links every character and voice to the central character library at `<repo-root>/assets/character-library/` (repo-root derived from the run-dir, not a hardcoded path), and if a character isn't in the library yet, adds it first then links. Use when the user wants to remix one of their existing ads — this skill produces the source JSON that the script-rewriting step and `remix-ad` consume.

extract-source-sample

This is an **agent-executed** skill. There are no Python scripts. The agent reads the run folder, builds the JSON, and stamps catalog links by hand. The content-goose run folders aren't always cleanly structured (some have empty production/ JSON, some carry everything in working/) — an agent adapts, a script would brittle out.

When to use

• "Extract the source-sample.json for `<run>`."
• "Get the upload-sample JSON for this ad so I can remix it."
• "Prep `<run>` for remix."

Do NOT use to:

• Rewrite the script for a new brand (that's a separate agent step that

consumes this skill's output).

• Render the remix (that's the existing `remix-ad` skill).
• Upload an ad to the library (that's `upload-ad-sample`).

Inputs

| Input | Required | Notes | |---|---|---| | `run-dir` | yes | Absolute path to a content-goose ad-run folder (e.g. `clients/ladder/ad-runs/run-02-podcast-skit`). | | `out` | no | Where to write the JSON. Default: `<run-dir>/remix/source-sample.json`. |

That's the entire interface.

What the agent must do

1. Read the run

Open each file if it exists; tolerate missing files (most production/*.json in older runs are empty stubs — fall back to `working/`):

• `working/script.json` — **primary source of truth** for scenes, voices, set.
• `production/asset-manifest.json` — `assets[]` with role `active_master`

points at the master mp4; per-asset `provider` + `metadata.model` produce the atom-skill rows.

• `HOW_TO_MAKE_THIS_VIDEO.md` — gets dumped verbatim into `how_to`.
• `video-project.json` — fallback for title / format when script.json doesn't

carry them.

• `finals/*.mp4` — fallback for the master mp4 if asset-manifest is empty.
• `working/characters/*.png` — anchor portraits per character.
• `working/*.py` — driver scripts (`render_vo.py`, `render_variants.py`,

`render_clips.py`, `stitch.py`, `build_end_card.py`, etc.). These are the source's runnable code; the remix consumer ports them. Capture in `production_scripts[]` (step 2 below).

**For sources with character-pose stills** (any run with a `working/characters/` folder of `<character>-<pose>.png` files — podcast-skit, founder-led, testimonial, recreate-ugc, etc.), audit every PNG with `file`. Do NOT stop at the base portraits. The recipe shot list references variant expression PNGs (e.g. `brittney-eyebrow-up.png`, `brad-phone-up.png`) by filename; the consumer assumes they exist on disk and will spend real money on lipsync calls before discovering they don't.

For sources without character-pose stills (music-video b-roll, abstract animated, product-only) — skip this audit; `variant_assets[]` stays empty.

Record per file (when auditing):

{
  "file":        "brittney-eyebrow-up.png",
  "pose_tag":    "skeptical-eyebrow",                  // slug from filename stem
  "kind":        "real" | "lfs-pointer" | "missing",
  "size_bytes":  142336
}

`file <path>` says `PNG image data, …` for real binaries and `ASCII text` for LFS pointers. A real binary is `>10KB` in practice; an LFS pointer is `<200 bytes`.

**Materialize LFS pointers before reading any binary.** An LFS pointer is a tiny (<200 byte) ASCII file beginning with `version https://git-lfs.github.com`. If a PNG or mp4 looks like one, run:

cd <run-dir-or-repo-root>
git lfs fetch --include=<relative path>
git lfs checkout <relative path>

before referencing it. **If `git lfs pull` no-ops and the LFS endpoint returns 404** (objects committed as pointers but never pushed — common on content-goose), leave the entry as `kind: "lfs-pointer"` in `variant_assets[]`. The consumer will regenerate or scrape; this skill does NOT fabricate. See [[feedback_lfs_pointer_audit_before_paid_calls]] and [[feedback_fal_subscribe_error_envelope]] for the downstream cost when this audit is skipped — Hume run-03 lost ~$3 + 25 min to it.

2. Build `source-sample.json`

Shape (every key always present, arrays may be empty):

{
  "title":              "<from script.json or video-project.json>",
  "format":             "video",
  "ratio":              "<aspect_ratio from script.json — e.g. 9:16>",
  "formatProfile":      "podcast-skit-fabricated",     // enum, see below
  "media_url":          "file://<abs path to master mp4>",
  "thumbnail_url":      null,
  "brand":              "<derive from path: clients/<brand>/ad-runs/...>",
  "tags":               [],
  "recipe":             { "shots": [...], "total_duration_sec": <int> },
  "extracted_script":   "HER: …\nHIM: …\n…",
  "skills_used":        ["generate-voiceover", "..."],     // atoms only
  "skills_source":      "measured" | "derived-from-production-scripts" | "inferred-canonical" | "guessed",
  "how_to":             "<contents of HOW_TO_MAKE_THIS_VIDEO.md or null>",
  "production_scripts": [
    { "path": "working/render_vo.py",       "role": "voiceover" },
    { "path": "working/render_variants.py", "role": "stills"    },
    { "path": "working/render_clips.py",    "role": "lipsync"   },
    { "path": "working/stitch.py",          "role": "stitch"    },
    { "path": "working/build_end_card.py",  "role": "end_card"  }
  ],
  "remix_spec": {
    "version":    1,
    "skills":     [{"slug": "...", "provider": "...", "mode