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 hyperfx-ai/marketing-skills --skill image-generationgit clone --depth 1 https://github.com/hyperfx-ai/marketing-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/hyperfx-ai/marketing-skills/image-generation)<a href="https://agentmods.dev/skills/hyperfx-ai/marketing-skills/image-generation"><img src="https://agentmods.dev/badge/skills/hyperfx-ai/marketing-skills/image-generation/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/hyperfx-ai/marketing-skills/image-generation"><img src="https://agentmods.dev/badge/skills/hyperfx-ai/marketing-skills/image-generation.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 | $0.00095 | $0.01034 |
| Opus 5 | $0.00048 | $0.00517 |
| Sonnet 5 | $0.00019 | $0.00207 |
| Haiku 4.5 | $0.00010 | $0.00103 |
Grade A, and why
image-generation 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 11d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image Generation
Generate images with the images_generate tool. It handles text-to-image,
image-to-image (pass reference_images), and multi-image composition. By default
(model="auto") it picks the best model for the request; set model to choose one.
Requirements
This skill assumes the Hyper MCP is connected to your
agent so the images_generate tool is available. For brand-consistent ad creative
work, Firecrawl must also be configured under your Hyper integrations.
Call shape
images_generate(
requests=[{"id": "ad1", "prompt": "A polished SaaS ad, clean composition"}],
aspect_ratio="16:9", # "1:1" (default), "9:16", "16:9", "4:5", "2:3", "3:2", "3:4", "4:3", "21:9", ...
quality="standard", # "draft" | "standard" | "high"
n=1, # 1-4 images per request
model="auto", # see "Choosing a model" below
)
- Image-to-image / brand references: put files in the request:
requests=[{"prompt": "Compose into a gift basket", "reference_images": ["file1", "file2"]}]. - Reproducible output: pass
seed=.... - Ground in real-world search: pass
use_search=True. - Do not display image URLs — they render automatically in chat.
Choosing a model
model="auto" is the right default. Override only when the task clearly calls for a
specific model:
| Task | model |
|---|---|
| First-pass concepts / quick ad ideation | gpt-image-2 |
| Image-to-image with references, high-resolution refinement, broad aspect ratios | nano-banana |
| Readable text inside the image (posters, labels, infographics) or search-grounded scenes | nano-banana-pro |
| Product photography, material/fabric fidelity, accurate spatial depth | seedream-4.5 |
See references/image-prompting.md for per-model prompt-writing tips.
Branded / website ad creatives — extract branding first
If the user gives a website URL and wants on-brand creatives:
What ships with it
3 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.
- 11d ago First seen · 90 lines · 95 tokens per session scan A 2c20c6164728
image-generation is a skill published in the GitHub repository hyperfx-ai/marketing-skills (85 stars, last pushed 16d ago), licensed MIT. It adds 95 tokens to every session and 1,034 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.
Other skills, from other repositories
creative-generate
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meta-ads
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creative-refresh
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creator-program
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podcast-marketing
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