scenario-ugc — Claude Code skill: what it does, how to install
Routes UGC-style creator video production with Scenario for TikTok, Reels, and Shorts. Turns scripts, portraits, and product photos into native 9:16 vertical clips.
npx skills add scenario-labs/skills --skill scenario-ugc -a claude-codeWhat scenario-ugc does
This skill orchestrates UGC-style video workflows using Scenario models. Instead of polished studio ads, it targets raw smartphone aesthetics: phone-height framing, natural skin texture, and available room light. The skill routes work into specialized lanes, delegating talking avatars or lip-sync to scenario-kling, narration to scenario-elevenlabs, and final composition to assembly tools.
Two operational constraints govern the workflow. The product itself is never generated from thin air; every demo insert requires an uploaded photo or video of the genuine item. In addition, the script cannot invent fake claims, statistics, or testimonials. The agent only speaks user-provided or user-approved copy.
Because this skill is a router, it depends entirely on sibling skills in the repository. If packages like scenario-video-assembly are missing, pipeline assembly halts until they are installed.
What it helps with
- route vertical 9:16 video generation across specialized Scenario tools
- generate talking-head creator ads from a portrait and approved script
- sync lip movements on existing footage to new audio tracks
- assemble product demo inserts from uploaded physical product shots
Example prompts for the agent
- Draft a 25-second UGC hook and prep a talking avatar lane using my uploaded portrait
- Create a vertical 9:16 faceless voiceover clip from this approved script and product photo
- Route this lip-sync request to Kling using my source video and clean audio track
How to install scenario-ugc
1Skills CLI (Vercel)
npx skills add scenario-labs/skills --skill scenario-ugc -a claude-code2Manually: put the files in place
~/.claude/skills/scenario-ugc/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/scenario-ugc/
1Skills CLI (Vercel)
npx skills add scenario-labs/skills --skill scenario-ugc -a codex2Manually: put the files in place
.agents/skills/scenario-ugc/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add scenario-labs/skills --skill scenario-ugc -a cursor2Manually: put the files in place
.cursor/skills/scenario-ugc/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install scenario-labs/skills scenario-ugc2Manually: put the files in place
.github/skills/scenario-ugc/SKILL.md1Skills CLI (Vercel)
npx skills add scenario-labs/skills --skill scenario-ugc -a gemini-cli2Platform CLI
gemini skills install https://github.com/scenario-labs/skills --path skills/scenario-ugcManually: put the files in place
.devin/skills/scenario-ugc/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add scenario-labs/skills --skill scenario-ugc -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about scenario-ugc
How do I install scenario-ugc in Claude Code?
Run npx skills add scenario-labs/skills --skill scenario-ugc -a claude-code in your terminal. You can also place the skill file directly at ~/.claude/skills/scenario-ugc/SKILL.md.
What does scenario-ugc do?
It structures UGC creator video pipelines for Scenario, routing tasks to specialized sibling models for avatars, voiceovers, and editing. It enforces 9:16 mobile framing and a six-beat pacing structure.
Can the skill invent product shots or customer reviews?
No. The rules forbid generative synthesis of unverified products and fake testimonials. Product inserts require real uploaded photos, and scripts use only verified, user-supplied text.
Source text
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