Suede Ad Creative

Suede Ad Creative

Use this Suede 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)

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. 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.

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; for hook generation and funnel-stage diagnosis inside any mode, load references/hook-system.md.

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

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)

RSA rules:

• Headlines must make sense independently and in any combination
• Pin headlines to positions only when necessary (reduces optimization)
• Include at least one keyword-focused headline
• Include at least one benefit-focused headline
• Include at least one CTA headline
• The quantities above are Google's ceiling. When the request came through suede-ads, its RSA output spec mandates the full 15 headlines and 4 descriptions, ship all of them.

Meta Ads (Facebook/Instagram)

LinkedIn Ads

TikTok Ads

Twitter/X Ads

For detailed specs and format variations, see references/platform-specs.md.

Generating Ad Visuals

For static ad structure, use the 15-template library in references/static-ad-templates.md, layout frameworks (Us vs. Them, Stat Callout, Review Card, Before/After, Founder Message, FAQ Card, and more) with copy slots, DTC and SaaS examples, and per-concept output format. Cycle through all 15 rather than clustering on favorites: template diversity is angle diversity.

When the concept is an iOS-native reveal video, an iMessage thread, a ChatGPT answer, an Apple Notes confessional, or an AirDrop share, where the screen surface itself is the ad, read references/imessage-video-ads.md before scripting. It carries surface selection, concept angles, pacing rules, production routes, and the compliance rules for dramatized conversations.

When the concept is a faceless motion/explainer video (15–45s, generated stills → image-to-video motion → TTS → captions), read references/motion-video-ads.md before writing prompts. It carries the pipeline, the visual-style library with fill-in prompt formulas, the brand-slots contract, and the QC gotchas.

When you need to pick an image, video, voice, or code-based generation tool, or price a batch, read references/generative-tools.md. It owns the tool-class roster (described by capability, not brand), per-placement image specs, and cost comparisons; those age, so use its numbers rather than any you remember.

Recommended workflow for scaled production:

1. Generate hero creative with AI tools (exploratory, high-quality)
2. Build Remotion templates based on winning patterns
3. Batch produce variations with Remotion using data feeds
4. Iterate: AI for new angles, Remotion for scale

Generating Ad Copy

Step 1: Define Your Angles

Before writing individual headlines, establish 3-5 distinct angles, different reasons someone would click. Each angle should tap into a different motivation.

Common angle categories:

Step 2: Generate Variations per Angle

For each angle, generate multiple variations. Vary:

• Word choice: synonyms, active vs. passive
• Specificity: numbers vs. general claims
• Tone: direct vs. question vs. command
• Structure: short punch vs. full benefit statement

At volume (10+ variations), close with a wild-card pass: 3-5 concepts on angles nobody asked for: contrarian, emotional, uncomfortably specific. These are where the outliers come from, and they cost one extra pass.

Step 3: Validate Against Specs

Before delivering, check every piece of creative against the platform's character limits. Flag anything that's over and provide a trimmed alternative.

Step 4: Organize for Upload

Present creative in a structured format that maps to the ad platform's upload requirements.

Iterating from Performance Data

When the user provides performance data, follow this process:

Step 1: Analyze Winners

Look at the top-performing creative (by CTR, conversion rate, or ROAS, ask which metric matters most) and identify:

• Winning themes: What topics or pain points appear in top performers?
• Winning structures: Questions? Statements? Commands? Numbers?
• Winning word patterns: Specific words or phrases that recur?
• Character utilization: Are top performers shorter or longer?

Step 2: Analyze Losers

Look at the worst performers and identify:

• Themes that fall flat: What angles aren't resonating?
• Common patterns in low performers: Too generic? Too long? Wrong tone?

Name the underperformers explicitly and say why the angle failed. Do not open by praising the existing set, and do not soften a losing angle into "needs more testing" when the declared metric has already resolved it at sufficient volume, say it lost, and retire it.