How to use
- Copy the installation command and review the source package before installing it in your own agent environment.
- Check the source document for required tools, credentials and supported hosts.
- Describe your input and desired deliverable; review the output against the source material.
npx skills add https://github.com/hyperfx-ai/marketing-skills --skill image-generationBefore you start
- Installation does not include a model subscription, API allowance or permission to access your files.
- Repository stars and installation counts describe activity, not an independent quality or security test.
Rendered from the source repository. Headings, examples, code, tables, links, and referenced images are preserved.
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:
- Call
firecrawl_branding_extractwith the URL → returns brand colors, fonts,
personality/tone, and saved image files (logo, favicon, og_image).
- Optionally
firecrawl_urls_scrapewithformats=["screenshot"]for visual context. - Write the prompt using the actual hex colors, font names, and tone, and pass the
logo file_id in reference_images.
The branding result's file field is a JSON data file, NOT an image — never pass it as a reference. Only logo.file_id and images.*.file_id are usable images.
Higher-level workflows
For multi-shot product or marketplace work, prefer the workflow tools — they preserve product identity and return structured results:
images_product_photoshoots_create— multi-shot product photography (studio,
lifestyle, hero, carousel, ad pack). See references/product-photoshoot.md.
images_marketplace_cards_create— marketplace listing image sets (Amazon main +
secondary, A+ modules, Shopify). See references/marketplace-cards.md.
Reminders
- Do NOT display image URLs to the user — they show automatically in chat.
- Refine vague prompts unless the user wants verbatim generation.
- Match
aspect_ratioto intent (social, print, web). - Use
quality="high"for production,"draft"/"standard"while iterating. - Generated
file_ids can be reused asreference_imagesin later calls. - For website brand work, call
firecrawl_branding_extractbefore generating.


