/suede-ad-creative

name: suede-ad-creative
description: "Suede AI paid-media creative system: angles, hooks, headlines, primary text, RSA and Performance Max assets, static and motion concepts, iOS-native reveal videos, grounded batch production, client review pages, and iteration from performance data, all checked against platform character limits. Use when producing or iterating ad creative for Google, Meta, LinkedIn, TikTok, or X from real product, review, and performance inputs. NOT FOR: campaign budgets, bidding, or targeting (use suede-ads), statistical test design (use suede-ab-testing), organic social posts (use suede-social), general marketing images such as OG or hero graphics (use suede-image), or landing-page copy (use suede-copy)."
metadata:
  version: 2.9.0

Suede AI Ad Creative

Use this performance-creative system to generate testable headlines, descriptions, primary text, and visual concepts, then iterate from real performance data.

Before Starting

**Check for product marketing context first:** If `.agents/product-marketing.md` exists (or `.claude/product-marketing.md`, or the legacy `product-marketing-context.md` filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.

Gather this context (ask if not provided):

1. Platform & Format

• What platform? (Google Ads, Meta, LinkedIn, TikTok, Twitter/X)
• What ad format? (Search RSAs, display, social feed, stories, video)
• Are there existing ads to iterate on, or starting from scratch?

2. Product & Offer

• What are you promoting? (Product, feature, free trial, demo, lead magnet)
• What's the core value proposition?
• What makes this different from competitors?

3. Audience & Intent

• Who is the target audience?
• What stage of awareness? (Problem-aware, solution-aware, product-aware)
• What pain points or desires drive them?

4. Performance Data (if iterating)

• What creative is currently running?
• Which headlines/descriptions are performing best? (CTR, conversion rate, ROAS)
• Which are underperforming?
• What angles or themes have been tested?

5. Constraints

• Brand voice guidelines or words to avoid?
• Compliance requirements? (Industry regulations, platform policies)
• Any mandatory elements? (Brand name, trademark symbols, disclaimers)

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How This Skill Works

This skill supports four modes:

Mode 1: Generate from Scratch

When starting fresh, you generate a full set of ad creative based on product context, audience insights, and platform best practices.

Mode 2: Iterate from Performance Data

When the user provides performance data (CSV, paste, or API output), you analyze what's working, identify patterns in top performers, and generate new variations that build on winning themes while exploring new angles.

The core loop:

Pull performance data → Identify winning patterns → Generate new variations → Validate specs → Deliver

Mode 3: Scaled Static Batches (Grounded)

For recurring static ad production at volume (e.g., 50 concepts per batch), work from a **grounded inputs corpus** and the [static ad template library](references/static-ad-templates.md). Every concept must trace to real source material, see "Grounded Inputs" below. To run this on a daily or weekly cadence, route the production loop to `suede-marketing-loops`. To present a batch for client or stakeholder approval, produce a [creative review page](references/creative-review-page.md).

Mode 4: Creative Strategy Loop

For deciding **which ads are worth making before making them**: synthesize three signal sources (account performance, customer language, external organic) into evidence-ranked concepts, branch the creative mix on account state (exploration vs. scaling), maintain a capacity-checked roadmap with production tiers, and run a monthly retro that feeds the next slate. The full system lives in [references/creative-roadmap.md](references/creative-roadmap.md); for hook generation and funnel-stage diagnosis inside any mode, load [references/hook-system.md](references/hook-system.md).

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Grounded Inputs

Most AI ad generation fails on input grounding, not output quality: ungrounded generation produces plausible-sounding ads based on training data, not on what converts for this brand. For scaled production (Mode 3), maintain a durable inputs corpus:

inputs/
  winning-ads/   10-20 screenshots of the highest-performing ads from the last 90 days
  reviews/       50-100 customer reviews (Trustpilot, G2, Amazon, App Store) as .md/.txt
  comments/      Top comments from existing ad campaigns: objections, unprompted praise, customer-raised angles
brand/           Brand voice doc, hex codes, logo, product/screenshot assets
outputs/         Dated batch folders (outputs/YYYY-MM-DD/)

**Why each input matters:**

• **Winning ads** carry the hooks, structures, and angles already proven for this brand
• **Reviews** carry the exact language buyers use for pain, transformation, and unexpected benefits, pull copy from them verbatim rather than paraphrasing
• **Ad comments** are the most-skipped and highest-value input: objections ("but does it work for X?") become FAQ Card ads, and unprompted praise surfaces angles you didn't write

**Grounding rules:**

• Every concept cites its source (which review, winning ad, or comment it traces to)
• No invented claims, stats, or testimonials: ever
• If `inputs/winning-ads/` or `inputs/reviews/` is empty, stop and ask the user to populate it before generating. Do not generate ungrounded concepts as a fallback.
• Inputs decay: refresh `inputs/winning-ads/` as new ads scale; refresh `inputs/reviews/` and `inputs/comments/` monthly

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Platform Specs

Platforms reject or truncate creative that exceeds these limits, so verify every piece of copy fits before delivering.

Google Ads (Responsive Search Ads)

| Element | Limit | Quantity | |---------|-------|----------| | Headline |