Ad Creative Generation

Ad Creative Generation

Generate ad creatives — both visuals and copy — that match a brand's identity. This skill orchestrates brand extraction, copywriting, and image generation into a single workflow.

Requirements

This skill assumes the Hyper MCP is connected to your agent so the tools below are available. Brand extraction also requires Firecrawl to be configured under your Hyper integrations.

How to run the tools in this skill

Every tool in this skill is named by its canonical tool name. Run it with the call your surface gives you:

If a tool is not found: in Hyper chat, find its toolkit id with discover_toolkits and pass it to enable_toolkit. On an MCP client or the CLI, tell the user which integration to connect in Hyper.

Tool surface

For deeper image-tool selection guidance see the image-generation skill. To turn finished creatives into running campaigns see google-ads and meta-ads.

Out of scope

• Picking the right image model when the task isn't ad creative — use image-generation.
• Creating, launching, or budgeting actual ad campaigns — use google-ads, meta-ads, tiktok-ads, pinterest-ads, or amazon-ads.
• Searching live competitor ads — use meta-ads-library. To read the Meta ads of a known competitor by its domain or Meta page in one query, use marketing_queries_start with the ads.meta_library stream; a repeated query within a day reads the stored run at no cost.

Critical rules

• Always extract branding first before generating visuals for a website/brand.
• Always pass both logo and screenshot as reference images — SVG logos are auto-converted to PNG.
• DO NOT display image URLs — they are automatically shown in chat.
• Read brand.screenshot.description to understand the product — never guess from the company name.
• Default to logo + headline + product screenshot for SaaS / product ads unless the user requests a different style.
• Use model="gpt-image-2" for the initial ad creative by default — with reference images for branded work, or without for loose first-pass concepts.
• Do not default to model="nano-banana-pro" for the first creative pass — use it when the user explicitly asks or when readable text inside the image is the main requirement.
• Set aspect_ratio to the placement — 1:1 (feed), 9:16 (story), 16:9 (banner) — and quality ("draft" | "standard" | "high"); images_generate takes those, not raw pixel sizes.
• Respect character limits — Google RSA headlines are 30 chars, descriptions 90 chars; Meta primary text is 125 chars visible.
• Change one variable per variant so test results are attributable.
• Match aspect ratio to placement — feed is 1:1, story is 9:16 (use 1024x1536), banner is 16:9 (use 1536x1024).

Routing Table

Based on what the user needs, read the appropriate reference file:

When the task spans multiple areas (e.g., "create ad creatives for this website"), follow the full workflow below.

Core Workflow

Phase 1: Brand Extraction

Call firecrawl_branding_extract with the website URL. This single call returns branding data, a saved logo, and a website screenshot:

pythonCopy code
brand = firecrawl_branding_extract(url="https://example.com")

The result contains:

• brand.logo.file_id — logo image to use as reference
• brand.screenshot.file_id — website screenshot to use as reference
• brand.branding — colors, typography, spacing, personality

Both logo and screenshot are saved images ready to pass as reference_images to image generation tools. SVG logos are automatically converted to PNG, so always include the logo — no need to filter by format.

Read references/brand-extraction.md only when the site gives no logo, colors or screenshot.

Phase 2: Ad Copy Generation

Using the brand personality, tone, and value proposition from the extraction, write ad copy variants. Structure copy by what's being tested:

1. Hook variants — different opening angles (number-led, question, pain-point, benefit-first)
2. Body variants — different messaging (social proof, feature highlight, urgency)
3. CTA variants — different calls to action (Learn More, Shop Now, Get Started, Try Free)

Change only one element per variant so results can be attributed to specific changes.

The platform file of Phase 4 holds the copy rules of its placements. Read references/ad-copy-frameworks.md only for copy with no placement.

Phase 3: Visual Creative Generation

Generate ad images using brand assets as references. Always pass both logo and screenshot as reference images — SVG logos are automatically converted to PNG by the branding tool.

Read brand.screenshot.description to understand what the product actually does and what the UI looks like. Do not guess from the company name.

Preferred first pass: images_generate with model="gpt-image-2"

Default approach (best practice for SaaS / product ads)

By default, a strong ad creative has three layers: brand (logo), copy (headline), and product (realistic screenshot showing the core value prop). This is the recommended starting point when the user hasn't specified a creative direction. Use model="gpt-image-2" for this initial composition unless the user explicitly asks for another model or the image is primarily a text-rendering task.

pythonCopy code
images_generate(
    requests=[{
        "prompt": (
            "Social media ad creative for [company name]. "
            "Top: the [company] logo. "
            "Headline: '[headline from ad copy phase or site hero text]'. "
            "Below the headline: a clean, realistic product screenshot of [describe the actual UI "
            "based on brand.screenshot.description]. "
            "Match the brand style from the references."
        ),
        "reference_images": [brand.logo.file_id, brand.screenshot.file_id]
    }],
    model="gpt-image-2",
    aspect_ratio="1:1",
    quality="high"
)

Following user direction

If the user asks for a specific creative style (lifestyle imagery, abstract, illustration, people using the product, etc.), follow their direction. The defaults above are a starting point, not a constraint. The reference images still supply brand consistency regardless of creative direction.

Things to avoid (unless the user specifically asks)

• Made-up visual elements (robot mascots, random platform logos, abstract graphics)
• UI chrome (buttons, nav bars, form inputs) — ad platforms add their own CTAs
• Guessing what the product does from the company name
• Generic marketing clip art

Visual Tool Selection

Platform Quick Reference

For full platform specs, read references/google-ads-creatives.md or references/meta-ads-creatives.md.

Ad Copy Variant Quick Reference

When generating variants from existing top performers:

1. Identify the winning elements: hook type, CTA, messaging angle, format
2. Generate variants that preserve winning elements while changing one variable
3. Group variants by what's being tested (hook, body, CTA)
4. Pair variants for A/B testing — each pair should isolate one variable

For detailed frameworks, read references/ad-copy-frameworks.md.

• Follow user direction — if the user wants lifestyle, illustration, abstract, or any other style, follow their lead. The reference images still supply brand consistency regardless of creative direction.