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.

Tool surface

The cmo_* tools are conditional — they appear when the CMO toolkit is enabled in the workspace. When absent, skip them and use the reference frameworks; do not fail the workflow.

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 save and monitor competitor ads cross-platform (Meta + Google Ads Transparency, durable creatives, longevity ranking), use cmo_ads_track / cmo_ads_tracked_list when the CMO toolkit is enabled.

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:

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.

For deeper guidance on brand extraction, read `references/brand-extraction.md`.

Phase 2: Ad Copy Generation

If `cmo_ad_brief` is available, call it first — it returns ranked ad archetypes for the brand's goal and platform with the reasoning, hook examples, structure beats, dos/donts, and a generation prompt already grounded in the brand's saved data:

brief = cmo_ad_brief(goal="drive conversions", platform="meta", url="https://example.com")

Build the copy variants and visual direction on the recommended archetypes (brief.recommendations[n].prompt is a ready generation prompt; unresolved placeholders are listed per recommendation). If the tool is not available, work from references/ad-copy-frameworks.md instead.

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.

For copy frameworks, hook patterns, and variant strategy, read `references/ad-copy-frameworks.md`.

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.

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.