Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add SupercmoHQ/superCMO-skills --skill generating-ugc-videosgit clone --depth 1 https://github.com/SupercmoHQ/superCMO-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/supercmohq/supercmo-skills/generating-ugc-videos)<a href="https://agentmods.dev/skills/supercmohq/supercmo-skills/generating-ugc-videos"><img src="https://agentmods.dev/badge/skills/supercmohq/supercmo-skills/generating-ugc-videos/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/supercmohq/supercmo-skills/generating-ugc-videos"><img src="https://agentmods.dev/badge/skills/supercmohq/supercmo-skills/generating-ugc-videos.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00131 | $0.03559 |
| Opus 5.5 | $0.00052 | $0.01424 |
| Sonnet 5.5 | $0.00026 | $0.00712 |
| Haiku 4.5 | $0.00013 | $0.00356 |
Grade A, and why
generating-ugc-videos scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
UGC Video
Turn a product into a video of a creator on camera.
Videos that do not have a person on camera with a product are out of scope — those should be generated by the generating-videos skill.
A produced advertisement in the brand's voice — a commercial, TV ad or brand film where the person plays a role in the brand's spot rather than a creator sharing their own take — belongs to generating-ad-videos, even when a presenter is on camera.
Settled for every job — never ask about these, and never re-decide them: the delivered video is
9:16 unless the user explicitly asked for 16:9 · the clip model is seedance-2.0-fast with audio
on, unless the user named one ·
storyboard sheets are gpt-image-2 at 16:9 · how many clips there are comes from the length · the
clips are always joined, hard cuts, no transitions.
Workflow
Step 1: Read what you have
- A product image or URL → hand it to
analyzing-productsfor what the product is, how a person physically uses it, which parts open or move, and what must stay identical wherever it appears. - Run
image_analysison every image supplied, not only the product — what each one shows, and whatever the product facts leave out. - What each image is for comes from the brief, not from its contents. A person in a frame does not make it a casting photo, and a second object does not make it a prop.
- No product → don't guess at one. It becomes the first thing Step 2 asks for.
Step 2: Interview
Skip this when the brief already settles the video — a clear product, a clear format, a length.
Otherwise ask once, bundled into a single message, always with a free-text way out.
| Ask | When |
|---|---|
| The product — a link or a photo | Neither was supplied. Offer to wait for an upload; a photographed product beats a described one. |
| How long | The brief doesn't say. Offer 15s, 30s, 45s or 60s, and let them type their own. |
| Whether the person in a supplied photo should be the creator | An image with a person in it was supplied and the brief doesn't say who they are. Ask rather than assume either way. |
| Whether they have a photo of the delivery package | The brief is about opening a package and none is attached. If they have one, wait for it to arrive before building anything — a promised photo is not a photo. If they don't, a plain unbranded box stands in. |
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 246 lines · 131 tokens per session scan A 9cc7b3c83ede
generating-ugc-videos is a skill published in the GitHub repository SupercmoHQ/superCMO-skills (242 stars, last pushed 6d ago), licensed Apache-2.0. It adds 131 tokens to every session and 3,559 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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