Goose Skills is a library of workflows and data APIs that lets coding agents handle growth and go-to-market work such as advertising, social media, content, SEO, lead generation, and customer research. It is intended for teams using Claude Code, Cursor, Codex, and similar agents. The catalogue entries are its reusable skills.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add gooseworks-ai/goose-skills --skill render-narrated-ugc-wardrobe-stitchgit clone --depth 1 https://github.com/gooseworks-ai/goose-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/gooseworks-ai/goose-skills/render-narrated-ugc-wardrobe-stitch)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/render-narrated-ugc-wardrobe-stitch"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-narrated-ugc-wardrobe-stitch/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/render-narrated-ugc-wardrobe-stitch"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-narrated-ugc-wardrobe-stitch.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00283 | $0.01328 |
| Opus 5 | $0.00142 | $0.00664 |
| Sonnet 5 | $0.00057 | $0.00266 |
| Haiku 4.5 | $0.00028 | $0.00133 |
Grade A, and why
render-narrated-ugc-wardrobe-stitch scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
render-narrated-ugc-wardrobe-stitch
Assemble a narrated-UGC "stitch reply" ad from a config: a fast-cut vertical testimonial where a single spoken VO carries a verbatim ~13-sentence reversal-hook monologue over ONE creator across ~5 wardrobe changes in ~3 micro-worlds, interspersed with product B-roll (capsule macro, unboxing, a landing-page scroll), ~30 hard cuts on the VO cadence, closing on a brand end card. This capability is the FREE, deterministic assembly — trim-to-EDL, hard-concat, the VO+music mix, the karaoke-pop caption burn, the landing-page zoompan, and the end-card append.
scripts/config.example.json is the worked example (Bioma "Do NOT buy Bioma Probiotics", ~37s
1080×1920 9:16, ~30 body cuts + a ~2s end card); scripts/PIPELINE.md maps every config block to
its source step and scripts/README.md documents the free assembly.
Run
This is the FREE, deterministic assembly stage — it spends nothing. The paid inputs are
separate capabilities — the spoken VO (create-vo-elevenlabs) Whisper-aligned so the WORD
BOUNDARIES set the cut grid; one locked creator (create-image-gpt-image-fal anchor + ~5 wardrobe
edits chained off the anchor) + 3 world wides + per-cut start-frames (create-image-fal product
composites); and one Veo/Seedance i2v clip per cut (create-video-fal). Given the VO +
vo-final.words.json + edl.json + one clip per cut + a Playwright landing-page PNG + the brand
end-card PNG, render-narrated-ugc-wardrobe-stitch trims each clip to its EDL window, hard-concats
on the VO cadence, mixes the VO over the ducked bed, burns the karaoke-pop captions, appends the
end card → the master. Re-cuts reuse the existing VO / start-frames / clips and cost $0.
Contract (the free assembly)
- The spoken VO carries the narrative — lock it FIRST. The VO IS the narration bed; the whole ad is cut to it. Never plan the cut grid before the VO is locked and Whisper-aligned.
- Build the EDL from the VO's Whisper word boundaries. ~30 role-tagged cuts (
hook,feature,reaction-insert,payoff-hold,b-roll-insert,landing-page); snap every cut window to the word boundaries. The payoff line gets a HELDpayoff-holdbeat (~3× mean shot length). - Hard cuts via
filter_complex concat, not the demuxer. Trim each clip to its EDL window and hard-concat withfilter_complex concat— the-f concatdemuxer drops the audio when a drawtext/scale step shaves a clip a few ms below its window. No dissolves. - Karaoke-pop captions on every word, throughout. From the VO's
vo-final.words.json(VEED Whisper preset, bold yellow), on every word; re-spell brand tokens Whisper mishears against the locked script ("synbiotic" over "symbiotic"; keep "I'ma" verbatim) — never edit the script to match Whisper. Captions are suppressed over the end card. If VEED mis-captions a brand token, hand-patch that sentence with local ASS karaoke. - Product B-roll breaks up the talking head. Capsule macro, unboxing, and a landing-page scroll are interspersed with the creator cuts. The landing-page scroll is FFmpeg zoompan over a Playwright-rendered PNG — not an i2v clip (i2v hallucinates the UI).
- VO over a ducked bed. Mix the optional instrumental bed sidechain-ducked UNDER the VO (−20dB, 20:1) so the VO stays clearly on top; the bed can drop in on the payoff beat.
- End card via the brand's real PNG — never AI-render brand text. Append the brand's real end-card PNG (~2s) on the tail, captions suppressed. A diffusion model garbles a wordmark.
- FFmpeg composite, deterministic, FREE. Trim-to-EDL,
filter_complex concat, VO+music mix, caption burn, landing-page zoompan, end-card append,loudnorm I=-14→ a 1080×1920 h264+aac master (~37s). No paid calls, no keys.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 57 lines · 283 tokens per session scan A b959a584fe7f
render-narrated-ugc-wardrobe-stitch is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 283 tokens to every session and 1,328 once invoked, about $0.0014 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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