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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-stitch

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

84/100

Category

Other

Supported Platforms

Universal

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.

Substance
29/30
Structure
12/20
Description
15/15
Adoption
13/20
Freshness
15/15

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.

SkillScoreStarsUpdatedFormat
render-narrated-ugc-wardrobe-stitch (this skill)by gooseworks-ai841.2k33d agoSKILL.md
LocalAIby mudler10050.0ktodayMCP Server
algorithmic-artby anthropics100177.9k33d agoSKILL.md
pptxby anthropics100177.9k33d agoSKILL.md
designby nextlevelbuilder100137.2k11d agoSKILL.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.

name: 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 HELD payoff-hold beat (~3× mean shot length).
  • Hard cuts via filter_complex concat, not the demuxer. Trim each clip to its EDL window and hard-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.
  • 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.

Related Skills

View on GitHub
GitHub Stars1.2k
CategoryOther
Updated1mo ago
Forks208

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions