scenario-ugc — скилл для Claude Code: что делает и как установить
Скилл маршрутизирует создание UGC-видео для соцсетей через Scenario. Помогает собрать ролик с говорящей головой, распаковкой или закадровым голосом в формате 9:16.
npx skills add scenario-labs/skills --skill scenario-ugc -a claude-codeЧто делает scenario-ugc
Скилл организует пайплайн генерации пользовательских видеороликов под TikTok, Reels и Shorts. Он не делает студийную рекламу, а воспроизводит эстетику съёмки на камеру телефона с естественным светом. Процесс строится по дорожкам: аватары с синхронизацией губ через scenario-kling, закадровый голос с b-roll через scenario-elevenlabs и scenario-video, а также финальный монтаж слоёв.
Здесь действуют два строгих правила. Продукт никогда не генерируется с нуля: предметные врезки требуют исходного фото или видео реального товара. Текст тоже нельзя выдумывать — агент произносит только реплики от пользователя, без фальшивых отзывов и обещаний.
Скилл сам не генерирует видеоряд, а распределяет задачи между соседними инструментами репозитория. Если в окружении нет связанных скиллов сборки или озвучки, агент остановит пайплайн.
Какие задачи решает
- маршрутизировать генерацию UGC-роликов под формат 9:16
- собрать видео с говорящим аватаром из портрета и текста
- синхронизировать движение губ на исходном видео под новую дорожку
- встроить кадры реального продукта в вертикальное промо
Примеры запросов агенту
- Сделай черновик UGC-ролика на 25 секунд для Shorts и настрой дорожку говорящего аватара по приложенному фото
- Собери видео распаковки: используй загруженные фотографии продукта и наложи закадровый голос
- Настрой липсинк для видео по приложенному аудиофайлу через scenario-ugc
Как установить scenario-ugc
1Через Skills CLI (Vercel)
npx skills add scenario-labs/skills --skill scenario-ugc -a claude-code2Вручную: положить файлы
~/.claude/skills/scenario-ugc/SKILL.mdПапка скилла целиком; для одного проекта — .claude/skills/scenario-ugc/
1Через Skills CLI (Vercel)
npx skills add scenario-labs/skills --skill scenario-ugc -a codex2Вручную: положить файлы
.agents/skills/scenario-ugc/SKILL.mdДля всех проектов — ~/.agents/skills/
1Через Skills CLI (Vercel)
npx skills add scenario-labs/skills --skill scenario-ugc -a cursor2Вручную: положить файлы
.cursor/skills/scenario-ugc/SKILL.mdCursor читает и .agents/skills/, и .claude/skills/
1GitHub CLI
gh skill install scenario-labs/skills scenario-ugc2Вручную: положить файлы
.github/skills/scenario-ugc/SKILL.md1Через Skills CLI (Vercel)
npx skills add scenario-labs/skills --skill scenario-ugc -a gemini-cli2Командой платформы
gemini skills install https://github.com/scenario-labs/skills --path skills/scenario-ugcВручную: положить файлы
.devin/skills/scenario-ugc/SKILL.mdСтарый путь .windsurf/skills/ тоже читается
Через Skills CLI (Vercel)
npx skills add scenario-labs/skills --skill scenario-ugc -a opencodeClaude.ai
Упакуйте папку в zip и загрузите в Settings → Capabilities → Skills
По шагам, с командами для каждой платформы: Как установить скилл в Claude Code: пять способов
Вопросы о scenario-ugc
Как установить scenario-ugc в Claude Code?
Выполните в терминале команду npx skills add scenario-labs/skills --skill scenario-ugc -a claude-code. Также можно вручную сохранить файл по пути ~/.claude/skills/scenario-ugc/SKILL.md.
Что делает scenario-ugc?
Скилл координирует производство вертикальных UGC-роликов через сервис Scenario. Он разбивает сценарий на шесть смысловых частей и вызывает нужные модели для генерации речи, анимации лица или монтажа.
Можно ли сгенерировать рекламу без фото реального продукта?
Нет, правила скилла прямо запрещают выдумывать внешний вид товара нейросетью. Демонстрационные кадры всегда строятся на основе загруженного пользователем фото или исходного видео.
Исходный текст
SKILL.md · MIT
---
name: scenario-ugc
description: "Use when producing UGC-style creator video with Scenario: a talking-head ad or testimonial from a portrait and a script, a founder clip, a product demo or unboxing, a reaction or before-after cut, a faceless voiceover over b-roll, or vertical social video for TikTok, Reels, or Shorts that must feel filmed on a phone rather than produced. Keywords: UGC, creator ad, talking head, testimonial, avatar, lipsync, founder video, social proof, faceless voiceover, organic, vertical video."
license: MIT
---
# Scenario UGC Creator Video
## Overview
UGC is a register, not a length: content that reads as a person talking into their own phone, not a
brand talking through a camera crew. Everything in this skill serves that register, and most
failures come from importing ad craft into it. This skill routes the production; mechanics live in
the sibling skills named per lane. Connection and the core loop: see the `scenario` skill in this
repo. If a sibling skill named here is missing from your available skills, ask the user to install
it (`npx skills add scenario-labs/skills --skill <name>`); unattended, proceed from tool schemas and
flag the gap.
Two rules are non-negotiable. The product is never generated: demo shots start from an uploaded
photo or footage of the real product (`scenario-product-shots` for stills). And the words are never
invented: no fabricated testimonials, review counts, metrics, medical or financial claims, or legal
copy; speak only lines the user supplied or approved, and prefer observable statements ("the texture
looks lighter") over claims ("this cures acne").
## Quick reference: route by speaker lane
Discover members with `recommend`, passing the lane's capability and the user's own words: it ranks
by measured cost and latency and names the purpose-built pick, where a capability-worded `search`
returns hundreds of keyword hits with nothing to choose between them. Read `next_step` before taking
a pick, per the `scenario` skill. Keep `search` for a member you can already name. Never assert a
generative model's id as a constant. Scenario's own single-purpose tool models are named outright
below: there is exactly one of each, so discovering them would only re-derive a constant.
| Lane | Route | Contract |
| ----------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------- |
| Portrait plus speech audio | Talking-avatar members (`img2video`) | `scenario-kling` |
| Existing footage, new words | Lipsync members (`video2video`), audio or `text`, never both | `scenario-kling` |
| Recorded delivery, different face | Motion-control members (`video2video`, say "motion control" in the `recommend` prompt or lipsync members come back): character still plus the recording as driving `video`, `keepOriginalSound` keeps the voice; output lands at the member's own size, the still lending only its orientation | `scenario-kling` |
| Generated creator speaking natively | Native-audio video families (`txt2video`), dialogue in quotes with the delivery named (pace, tone, one gesture) | `scenario-veo`, `scenario-seedance`, `scenario-kling` |
| Faceless voiceover | B-roll clips (`txt2video`) plus TTS narration (`txt2audio`) | `scenario-video`, `scenario-elevenlabs` |
| Product demo inserts | Image-to-video off the uploaded product still | `scenario-product-shots`, `scenario-video` |
| Captions, cut, 9:16 master | Assembly tool models; text cards as image layers | `scenario-video-assembly`, `scenario-text-overlay` |
Script in six spoken beats: hook (one concrete tension or result), context (why this speaker cares),
product moment, proof (visible demo or a user-supplied fact), turn (objection answered or
before-after), close (soft CTA). Write for the mouth, not the page: contractions, false starts
allowed, no taglines. Spoken pace runs near 2.5 words a second, so a 25-second ad is roughly 60
words, but that is a first guess and avatar members undershoot it: observed rates run 1.76 to 1.94
words a second, and one member padded a 60-word script with 21s of silence instead. Time the
returned clip before assembling, then trim the silences where there are any and cut the script where
the delivery is simply slow. The compositor has no cut inside a layer, so a trim is one layer per
kept segment, each with its own `trimStart` and `duration`.
Keep the register in every visual prompt: phone-height framing, available light, a real location
with clutter, natural skin texture, one handheld drift at most. Cinematic grammar (dolly moves,
golden-hour rim light, shallow anamorphic looks, graded color, retouched skin) reads as an ad and
kills belief. Compose 9:16 natively, reframing any non-vertical still a lane consumes (the avatar
portrait, a motion-control character still, the product photo) per `scenario-formats` before
generating; a cropped 16:9 master frames like television.
## Worked example: 25-second founder ad from a portrait
1. Brief once: offer, platform, runtime, the facts the founder may claim, tone. Collect the portrait
and the real product photo, then run without stopping.
2. Create this run's collection before the first generation (`collection_create`, catalog write
lane, `name` only), then `collection_add_assets` each keeper as it lands; its returned
`itemCount` is the receipt.
3. Draft the six beats at about 60 words and confirm the wording with the user; the script is a
claims surface, not just copy. Unattended, keep every line to wording the brief already supplied
and cut any beat that would need a new claim.
4. Voice: `upload_asset` the founder's recorded narration, or generate TTS per `scenario-elevenlabs`
when they want a stand-in voice they approved.
5. `recommend` with `capability="img2video"` and the brief in the user's words; pick from its ranked
list by input contract (a script-taking member when the speech exists only as text, an
audio-taking one when a recording exists), then by whether the member can be told not to burn in
captions: some hallucinate gibberish subtitles that no negative prompt suppresses, and the ones
with a switch cost several times more, which `recommend` prices for you.
6. `upload_asset` the portrait. Prompt the speaker's hands empty and the set free of products:
avatar members invent props and label type. `model_run` with `dry_run=true` first: avatar members
sit far apart on price, so quote before spending.
7. Run for real with `wait=false`, then `jobs_wait` with the returned job id, re-called with
`pending_job_ids` on timeout, never a second `model_run`.
8. Demo insert: animate the uploaded product photo with an image-to-video member (`scenario-video`),
3 to 5 seconds, one micro-move. Whether `aspectRatio` survives a start image is per member and
the schema note can be wrong either way, so read `width` and `height` off the returned asset
rather than assuming the ratio held.
9. Assemble with `model_scenario-compose-video` per `scenario-video-assembly`: talking head as the
spine, insert cut over beats three and four, product-name card from `scenario-text-overlay` as an
image layer, dropped outright when no approved product name exists, since inventing one is the
fabrication the brief forbids. Every overlay layer needs an explicit `width` and `height` or the
compositor scales it, and `durationMode: "custom"` pins the master's length against image layers
that would stretch it. A succeeded compose job is not proof the layers drew or landed where sent:
pull a frame from the composite and confirm the card is present and placed before delivering.
Captions come last, on the finished cut, from the captioner `scenario-video-assembly` discovers
with `recommend` (two exist, so no fixed id), positioned inside the platform's safe zone: the
compositor has no text layer.
10. `asset_display` the master. Gate every generated clip, the talking head included: take the
frames from the platform, the free `firstFrame` and `lastFrame` off `asset_get` first and
`model_scenario-video-to-image-seq` when a mid-clip frame is what settles it, then verify them
against the uploads (`scenario-asset-analysis`); a local extraction is not a substitute at any
budget, because it yields no asset to file or audit and the gate stops being traceable; an
invented product in the speaker's hands or legible generated type fails a clip exactly like
label drift on the insert. Report spend by summing this run's own job records by job id:
`jobs_list` is project-scoped and over-reports, and `usage`'s headline figure is
project-lifetime.
## Common mistakes
- Writing ad copy and handing it to a mouth: alliterative taglines collapse on a talking head; read
the script aloud before generating.
- Fabricating social proof: an invented "10,000 five-star reviews" is a claim the user never made;
keep numbers and testimonials to supplied wording.
- Prompting the creator like a commercial: tripod framing, perfect light, and a spotless studio
kitchen read as an ad; imperfection is the format.
- Passing both audio and `text` to a lipsync member: exclusive inputs; pick one.
- Skipping `dry_run` on avatar and lipsync runs: per-member pricing varies too widely to guess.
- Generating the product or any on-screen text: the product comes from the uploaded photo, type is
overlaid in assembly. Native-speech members can caption the quoted line, and those captions
collide with the ones the captioner places in the safe zone, so end every dialogue prompt with "no
subtitles, no on-screen text". There the line is the only lever and costs nothing, and it is not a
guarantee (one native-speech member captioned anyway at authoring time), so pull a frame before
compositing; avatar members can hallucinate subtitles no prompt suppresses, which is why step 5
prices their caption switch.
- One 40-second b-roll or generated-creator take: generate per-beat clips and cut on the beat turns;
single long takes drift and cost more to retry. A talking-head script stays one take, trimmed in
assembly.
- Cropping a landscape master to 9:16: heads and captions land outside the safe zone; compose
vertical from the start.
© scenario-labs · MIT · scenario-labs/skills