Image generation

Skill hyperfx-ai/marketing-skills/skills/image-generation

Marketing skills for AI agents — paid ads, social media, SEO, competitor research, creative generation, email, analytics, and more. Powered by Hyper MCP.

Install

npx -y skills add hyperfx-ai/marketing-skills --skill image-generation

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Generate images through the Hyper MCP with the unified `images_generate` tool — text-to-image, image-to-image, and branded ad creatives — choosing the model (gpt-image-2, nano-banana, nano-banana-pro, seedream-4.5) per task. Use when the user asks to generate an image, create an ad creative, do an image-to-image edit, render text inside an image, or produce a print-quality poster.

SKILL.md

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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_extract with the URL → returns brand colors, fonts, personality/tone, and saved image files (logo, favicon, og_image).
Optionally firecrawl_urls_scrape with formats=["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_ratio to intent (social, print, web).
Use quality="high" for production, "draft"/"standard" while iterating.
Generated file_ids can be reused as reference_images in later calls.
For website brand work, call firecrawl_branding_extract before generating.