Assemble a narrated-UGC "stitch reply" ad from a config — a single spoken VO carries a verbatim testimonial while ~30 per-cut i2v clips (one creator across ~5 wardrobes in ~3 worlds, plus product B-roll) are each trimmed to their EDL window built from the VO's Whisper word boundaries and hard-concatenated via filter_complex concat (never the demuxer, which drops audio on a duration mismatch), the VO mixed over an optional sidechain-ducked instrumental bed (−20dB, 20 to 1) so the VO stays on top, karaoke-pop captions burned on every word throughout (VEED Whisper preset, re-spelled against the locked script), a landing-page scroll rendered as FFmpeg zoompan over a Playwright PNG (not i2v), and closed on the brand's real end-card PNG — never AI-rendered text. This is the FREE deterministic assembly stage (trim-to-EDL + filter_complex concat + VO and music mix + karaoke captions + landing-page zoompan + end-card append); the VO, creator, start-frames, and clips come from create-vo-elevenlabs / create-image-gpt-image-fal / create-image-fal / create-video-fal. Use for the narrated-ugc-wardrobe-stitch format.
npx skills add https://github.com/gooseworks-ai/goose-skills --skill 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.
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.
ad is cut to it. Never plan the cut grid before the VO is locked and Whisper-aligned.
hook, feature,reaction-insert, payoff-hold, b-roll-insert, landing-page); snap every cut window to the
word boundaries. The payoff line gets a HELD payoff-hold beat (~3× mean shot length).
filter_complex concat, not the demuxer. Trim each clip to its EDL window andhard-concat with filter_complex concat — the -f concat demuxer drops the audio when a
drawtext/scale step shaves a clip a few ms below its window. No dissolves.
vo-final.words.json (VEEDWhisper 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.
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).
(−20dB, 20:1) so the VO stays clearly on top; the bed can drop in on the payoff beat.
end-card PNG (~2s) on the tail, captions suppressed. A diffusion model garbles a wordmark.
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.
Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documentation, or social media posts.
Create and edit JSON Canvas files (.canvas) with nodes, edges, groups, and connections. Use when working with .canvas files, creating visual canvases, mind maps, flowcharts, or when the user mentions Canvas files in Obsidian.
Publish Markdown articles to X (Twitter) Articles editor with proper formatting. Use when user wants to publish a Markdown file/URL to X Articles, or mentions "publish to X", "post article to Twitter", "X article", or wants help with X Premium article publishing. Handles cover image upload and converts Markdown to rich text automatically.
Downloads videos from YouTube and other platforms for offline viewing, editing, or archival. Handles various formats and quality options.
Teaches the AI to design like a high-end agency. Defines the exact fonts, spacing, shadows, card structures, and animations that make a website feel expensive. Blocks all the common defaults that make AI designs look cheap or generic.
Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
Take gooseworks-ai/render-narrated-ugc-wardrobe-stitch from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.