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-image-adsgit 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-image-ads)<a href="https://agentmods.dev/skills/supercmohq/supercmo-skills/generating-image-ads"><img src="https://agentmods.dev/badge/skills/supercmohq/supercmo-skills/generating-image-ads/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-image-ads"><img src="https://agentmods.dev/badge/skills/supercmohq/supercmo-skills/generating-image-ads.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 | $0.00096 | $0.03299 |
| Opus 5 | $0.00048 | $0.01649 |
| Sonnet 5 | $0.00019 | $0.00660 |
| Haiku 4.5 | $0.00010 | $0.00330 |
Grade A, and why
generating-image-ads 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 9d 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 — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image ads
Turn a product into finished static ad(s).
Workflow
Step 1: Read what you have
- A product image or URL is available → hand it to
analyzing-products, and take four things back: what the product is; how a person physically uses it — sprayed, pumped, worn, etc.; which parts open or move, and in what order; and the details that have to stay identical wherever it appears. - Look at the image before you describe it. Take the product only from what the image shows. Run
image_analysisand ask it for everything this ad needs that the product facts leave out — how the surfaces take light, and whatever else the frame you end up building will have to describe. - Product not supplied → don't guess at the product. It becomes the first thing Step 2 asks for.
Step 2: Interview
Skip this entirely when the brief already makes the ad obvious — a clear product, a clear message, and nothing load-bearing missing. The interview exists to close real gaps, not to confirm what you were already told.
Otherwise ask once, bundled into a single message. Always leave a free-text way out so the user can answer off-menu. Anything this skill already settles is not a question.
Do ask, when the answer is genuinely missing:
| Ask | When |
|---|---|
| The product — what it is, and whatever else identifies it: packaging, colour, distinguishing features | No image and no URL. Offer to wait for an upload first; a photographed product beats a described one. |
| Brand guidelines, if they have any — the color palette, the art direction they shoot to, anything they never put in frame, the typeface where text is drawn, and the register they want the frame to read at | The brief mentions none and the packaging doesn't imply them. Treat as optional: plenty of brands have none, so take what they give and move on. |
| The offer and the one claim — a discount, a launch, a benefit, the words they can stand behind | The ad has to say something and the brief doesn't say what. Only claims the user supplies are used, and used as written. |
| Where it runs — the placement and the ratios | The brief doesn't say, and the placement decides the crop and the safe zones. |
What ships with it
2 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.
- 9d ago First seen · 193 lines · 96 tokens per session scan A 63e5e61ab9d9
generating-image-ads is a skill published in the GitHub repository SupercmoHQ/superCMO-skills (34 stars, last pushed 12d ago), licensed Apache-2.0. It adds 96 tokens to every session and 3,299 once invoked, about $0.0005 per session on Opus 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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