render-narrated-ugc-wardrobe-stitch
Assemble a narrated-UGC \"stitch reply\" ad from a config: one voiceover carries a verbatim testimonial while about 30 short clips (one creator across about 5 outfits and 3 settings, plus product B-roll) are trimmed to windows cut from the voiceover's word timings and joined.
Install / Use
npx skills add gooseworks-ai/goose-skills --skill render-narrated-ugc-wardrobe-stitchInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
OtherSupported Platforms
Our assessment of render-narrated-ugc-wardrobe-stitch
render-narrated-ugc-wardrobe-stitch scores 84/100 on our quality scale, 201st of 283 Other skills we index.
Its SKILL.md is 6.6 KB long, split into 4 sections with 1 code example: a thorough specification that gives an agent plenty to work with.
With 1,222 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated 33 days ago, so render-narrated-ugc-wardrobe-stitch is actively maintained.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
render-narrated-ugc-wardrobe-stitch compared with similar skills
All 4 of these similar skills score higher than render-narrated-ugc-wardrobe-stitch; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| render-narrated-ugc-wardrobe-stitch (this skill)by gooseworks-ai | 84 | 1.2k | 33d ago | SKILL.md |
| LocalAIby mudler | 100 | 50.0k | today | MCP Server |
| algorithmic-artby anthropics | 100 | 177.9k | 33d ago | SKILL.md |
| pptxby anthropics | 100 | 177.9k | 33d ago | SKILL.md |
| designby nextlevelbuilder | 100 | 137.2k | 11d ago | SKILL.md |
Frequently asked questions
- How do I install render-narrated-ugc-wardrobe-stitch?
- Run
npx skills add gooseworks-ai/goose-skills --skill render-narrated-ugc-wardrobe-stitch. The install tabs above show the steps for each supported agent. - Which AI agents does render-narrated-ugc-wardrobe-stitch work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is render-narrated-ugc-wardrobe-stitch safe to use?
- It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is render-narrated-ugc-wardrobe-stitch still maintained?
- The repository was last updated 33 days ago, so render-narrated-ugc-wardrobe-stitch is actively maintained.
Skill content
View source on GitHubname: render-narrated-ugc-wardrobe-stitch description: "Assemble a narrated-UGC "stitch reply" ad from a config: one voiceover carries a verbatim testimonial while about 30 short clips (one creator across about 5 outfits and 3 settings, plus product B-roll) are trimmed to windows cut from the voiceover's word timings and joined. The voiceover sits over a ducked music bed, word-by-word captions run throughout, a landing-page scroll is animated from a screenshot, and the brand's real end card closes it (never AI-rendered text). Free, deterministic assembly using the shared stitch-videos-ffmpeg helper; the voiceover, creator stills and clips come from create-vo-elevenlabs, create-image-gpt-image-fal, create-image-fal and create-video-fal. Use for the narrated-ugc-wardrobe-stitch format." status: active version: "1.0.2"
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 testimonial over ONE creator across ~5 wardrobe changes in ~3 micro-worlds, interspersed with product B-roll (e.g. product 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 word-by-word caption burn, the landing-page zoompan, and the end-card append.
scripts/config.example.json is one worked example (the fictional Sunnook "Don't buy Sunnook Tan Mousse" demo, ~37s
1080×1920 9:16, ~30 body cuts + a ~2s end card) — its creator, voice, hook, worlds and music are
that demo's answers, not defaults; scripts/PIPELINE.md maps every config block to
its source step and scripts/README.md documents the free assembly.
Choices
The creative calls are made upstream by the user (the recipe's choices) and arrive in the config;
this assembly never picks them.
- creator — who is on camera →
character.descriptor/character.name. The demo used a 31-year-old curly-haired woman. - voice — the narration voice →
vo.voice_id/vo.settings. The demo used an excited voice matched to its creator. - hook_angle — the testimonial's hook →
vo.hook_line/vo.script_md/vo.payoff_line. The demo used a "Don't buy <brand>" reversal. - worlds — the 3 places →
worlds.briefs. The demo used bedroom / bathroom / balcony. - music — the bed under the VO, or none →
audio_mix.music_brief. With "no music", skip the bed and mix the VO alone.
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 word-by-word captions, appends the
end card → the master. Re-cuts reuse the existing VO / start-frames / clips and spend nothing.
The assembly runs on a shared helper. montage.py lives in the shared stitch-videos-ffmpeg
atom, which this package lists in requires_skills, so it installs alongside. After
gooseworks fetch render-narrated-ugc-wardrobe-stitch it is at
/tmp/gooseworks-scripts/stitch-videos-ffmpeg/scripts/montage.py. Write a montage.json from the
config (field mapping in scripts/README.md), then:
M=/tmp/gooseworks-scripts/stitch-videos-ffmpeg/scripts/montage.py
python3 $M edl --spec montage.json --out edits/edl.json # check every clip + window first
python3 $M run --spec montage.json --out edits/master-final.mp4 --workdir edits/work
run builds the EDL from the VO word boundaries (word_range), trims and hard-cuts every clip
with the filter_complex concat filter, renders the landing-page scroll as a zoom/pan over the PNG,
holds the end-card PNG, burns word-by-word captions (one word at a time in one colour, with
respell; no active-word highlight), mixes the VO over the
ducked bed, and masters to -14 LUFS. It writes edits/work/manifest.json. This package ships no
scripts of its own.
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 (Sunnook demo: "Sunnook" over "Sunook"; kept "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. Product macro (Sunnook: mousse on a mitt), 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 as long as the VO plus the end card (~37 s in the demo). No paid calls, no keys.
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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
