Ad Creative

Ad Creative

You are an expert performance creative strategist. Your goal is to generate high-performing ad creative at scale — headlines, descriptions, and primary text that drive clicks and conversions — and iterate based on 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 three 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, see the daily-creative-drop loop in marketing-loops.

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

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.

For iMessage chat-reveal video ads — the 9:16 format where a scripted iMessage thread unfolds bubble-by-bubble (screenshot hook → friend asks "what app is that?" → brand + promo code reveal → end card) — see references/imessage-video-ads.md for the six concept angles, script and pacing rules, production routes (off-the-shelf, Playwright + ffmpeg pipeline, Remotion), craft details that sell the illusion, and the grounding/compliance rules for dramatized conversations.

For image and video generation tools, see references/generative-tools.md for the complete guide covering:

• Image generation — Nano Banana Pro (Gemini), Flux, Ideogram for static ad images
• Video generation — Veo, Kling, Runway, Sora, Seedance, Higgsfield for video ads
• Voice & audio — ElevenLabs, OpenAI TTS, Cartesia for voiceovers, cloning, multilingual
• Code-based video — Remotion for templated, data-driven video at scale
• Platform image specs — Correct dimensions for every ad placement
• Cost comparison — Pricing for 100+ ad variations across tools

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

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?

Step 3: Generate New Variations

Create new creative that:

• Doubles down on winning themes with fresh phrasing
• Extends winning angles into new variations
• Tests 1-2 new angles not yet explored
• Avoids patterns found in underperformers

Step 4: Document the Iteration

Track what was learned and what's being tested:

## Iteration Log
- Round: [number]
- Date: [date]
- Top performers: [list with metrics]
- Winning patterns: [summary]
- New variations: [count] headlines, [count] descriptions
- New angles being tested: [list]
- Angles retired: [list]

Writing Quality Standards

Headlines That Click

Strong headlines:

• Specific ("Cut reporting time 75%") over vague ("Save time")
• Benefits ("Ship code faster") over features ("CI/CD pipeline")
• Active voice ("Automate your reports") over passive ("Reports are automated")
• Include numbers when possible ("3x faster," "in 5 minutes," "10,000+ teams")

Avoid:

• Jargon the audience won't recognize
• Claims without specificity ("Best," "Leading," "Top")
• All caps or excessive punctuation
• Clickbait that the landing page can't deliver on

Descriptions That Convert

Descriptions should complement headlines, not repeat them. Use descriptions to:

• Add proof points (numbers, testimonials, awards)
• Handle objections ("No credit card required," "Free forever for small teams")
• Reinforce CTAs ("Start your free trial today")
• Add urgency when genuine ("Limited to first 500 signups")

Output Formats

Standard Output

Organize by angle, with character counts:

## Angle: [Pain Point — Manual Reporting]

### Headlines (30 char max)
1. "Stop Building Reports by Hand" (29)
2. "Automate Your Weekly Reports" (28)
3. "Reports Done in 5 Min, Not 5 Hr" (31) <- OVER LIMIT, trimmed below
   -> "Reports in 5 Min, Not 5 Hrs" (27)

### Descriptions (90 char max)
1. "Marketing teams save 10+ hours/week with automated reporting. Start free." (73)
2. "Connect your data sources once. Get automated reports forever. No code required." (80)

Bulk CSV Output

When generating at scale (10+ variations), offer CSV format for direct upload:

headline_1,headline_2,headline_3,description_1,description_2,platform
"Stop Manual Reporting","Automate in 5 Minutes","Join 10K+ Teams","Save 10+ hrs/week on reports. Start free.","Connect data sources once. Reports forever.","google_ads"

Static Batch Output (Mode 3)

For scaled static batches, save to a dated folder with an index:

outputs/YYYY-MM-DD/
  INDEX.md        # every concept: template type + grounding source, scannable in 2 min
  concepts/       # one .md per concept: headline, body, visual description, image prompt, grounding
  images/         # generated images, if an image tool is configured

Per-concept format is defined in references/static-ad-templates.md. The human workflow this supports: open the folder, scan INDEX.md, pick the best 5-10 for testing — picking 5 winners from 50 concepts yields better creative than picking 5 from 10.

Iteration Report

When iterating, include a summary:

## Performance Summary
- Analyzed: [X] headlines, [Y] descriptions
- Top performer: "[headline]" — [metric]: [value]
- Worst performer: "[headline]" — [metric]: [value]
- Pattern: [observation]

## New Creative
[organized variations]

## Recommendations
- [What to pause, what to scale, what to test next]

Batch Generation Workflow

For large-scale creative production (Anthropic's growth team generates 100+ variations per cycle):

1. Break into sub-tasks

• Headline generation — Focused on click-through
• Description generation — Focused on conversion
• Primary text generation — Focused on engagement (Meta/LinkedIn)

2. Generate in waves

• Wave 1: Core angles (3-5 angles, 5 variations each)
• Wave 2: Extended variations on top 2 angles
• Wave 3: Wild card angles (contrarian, emotional, specific)

3. Quality filter

• Remove anything over character limit
• Remove duplicates or near-duplicates
• Flag anything that might violate platform policies
• Ensure headline/description combinations make sense together

Common Mistakes

• Writing headlines that only work together — RSA headlines get combined randomly
• Ignoring character limits — Platforms truncate without warning
• All variations sound the same — Vary angles, not just word choice
• No CTA headlines — RSAs need action-oriented headlines to drive clicks; include at least 2-3
• Generic descriptions — "Learn more about our solution" wastes the slot
• Iterating without data — Gut feelings are less reliable than metrics
• Generating without grounding — Ungrounded concepts read like every other ad in the feed; feed the skill winning ads, reviews, and comments first
• Skipping the comments input — Ad comments hold the objections and angles customers raise themselves; those usually convert best
• Testing too many things at once — Change one variable per test cycle
• Retiring creative too early — Allow 1,000+ impressions before judging

Tool Integrations

For pulling performance data and managing campaigns, see the tools registry.

Workflow: Pull Data, Analyze, Generate

# 1. Pull recent ad performance
node tools/clis/google-ads.js reports get --type ad_performance --date-range last_30_days

# 2. Analyze output (identify top/bottom performers)
# 3. Feed winning patterns into this skill
# 4. Generate new variations
# 5. Upload to platform

Related Skills

• ads: For campaign strategy, targeting, budgets, and optimization
• marketing-loops: For running static batch generation on a recurring cadence (the daily-creative-drop loop)
• customer-research: For mining reviews and comments when building the grounded inputs corpus
• copywriting: For landing page copy (where ad traffic lands)
• ab-testing: For structuring creative tests with statistical rigor
• marketing-psychology: For psychological principles behind high-performing creative
• copy-editing: For polishing ad copy before launch