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-imagesgit 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-images)<a href="https://agentmods.dev/skills/supercmohq/supercmo-skills/generating-images"><img src="https://agentmods.dev/badge/skills/supercmohq/supercmo-skills/generating-images/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-images"><img src="https://agentmods.dev/badge/skills/supercmohq/supercmo-skills/generating-images.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 | $0.00115 | $0.02276 |
| Opus 5.5 | $0.00046 | $0.00910 |
| Sonnet 5.5 | $0.00023 | $0.00455 |
| Haiku 4.5 | $0.00012 | $0.00228 |
Grade A, and why
generating-images 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 — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image Generation
Turn a brief — text, optionally with reference image(s) — into a still image via the
image_generate tool. Two decisions drive quality: which model (always) and which format
recipe (only when the deliverable is a known one).
Before you route: if the brief is commercial product photography — a packshot, a product in
a styled scene, a hero or banner for a product, an on-model or try-on shot,
or restyling an existing product photo — hand off to the generating-product-photos skill, which
owns that surface. If it is a product advertisement — a headline, an offer or a call to action drawn
over the product, a promotional before/after or comparison — hand off to generating-image-ads. Stay here for everything else: general images, graphics and posters, portraits,
illustrations, cinematic stills, infographics, and one-off reference edits.
Workflow
Step 1: Route to a model
Route by what the image has to do — read the brief for intent. The descriptions below are signals to weigh, not a literal router — the examples are illustrative, not a checklist to match against. Then read the chosen model's prompt guide before writing anything.
| Route when the brief is about… | Model | Prompt guide |
|---|---|---|
| Rendering words legibly, or a designed layout where elements sit in deliberate positions — for example a poster, ad, banner, thumbnail, or infographic | gpt-image-2 |
references/prompt-gpt-image-2.md |
| A drawn or rendered look rather than a photograph — for example a cartoon, anime, illustration, flat vector, or 3D render | nano-banana-2 |
references/prompt-nano-banana.md |
| A convincing real person, or a photographic frame with deliberate cinematography — for example a creator or influencer portrait, UGC, or a film-like still | nano-banana-pro |
references/prompt-nano-banana.md |
| Altering a supplied image — for example swapping a background, removing or replacing an element, or restaging the scene | gpt-image-2 |
references/prompt-gpt-image-2.md |
| Altering a supplied image where a real face must stay recognisable | seedream-5 |
references/prompt-seedream.md |
What ships with it
9 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.
- evals/eval_cases.json 2.4 KB
- references/format-cinematic.md 1.2 KB
- references/format-portrait.md 3.8 KB
- references/format-poster.md 3.3 KB
- references/prompt-flux.md 1.0 KB
- references/prompt-gpt-image-2.md 1.3 KB
- references/prompt-grok.md 800 B
- references/prompt-nano-banana.md 3.0 KB
- references/prompt-seedream.md 1.4 KB
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 · 152 lines · 115 tokens per session scan A 7b25699b5608
generating-images is a skill published in the GitHub repository SupercmoHQ/superCMO-skills (242 stars, last pushed 6d ago), licensed Apache-2.0. It adds 115 tokens to every session and 2,276 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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