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Ad Creative

Free

Generate and optimize ad copy at scale.

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Free · Opens the source repo

What Ad Creative does

The Ad Creative skill is designed for marketers and advertisers who need to produce effective ad copy quickly and efficiently. It enables users to generate headlines, descriptions, and full ad variations tailored to various paid advertising platforms such as Google Ads, Facebook, and LinkedIn. By leveraging product context, audience insights, and platform-specific best practices, this skill helps create compelling ad content that drives clicks and conversions.

This skill operates in four distinct modes to accommodate different needs. The first mode allows users to generate ad creative from scratch, creating a complete set of ads based on the provided product and audience information. The second mode focuses on iterating existing ads by analyzing performance data to identify successful patterns and generate new variations that build on those insights. The third mode is for scaled production of static ads, where users can produce large batches of ad concepts grounded in real performance data and customer feedback. Finally, the fourth mode synthesizes various signal sources to prioritize which ads to create, ensuring that resources are allocated to the most promising concepts.

A key feature of the Ad Creative skill is its emphasis on grounded inputs, which ensures that generated ad copy is based on proven data rather than generic templates. Users are encouraged to maintain a library of winning ads, customer reviews, and comments to inform the creative process. This grounding not only enhances the relevance of the generated content but also helps avoid unsubstantiated claims and ensures compliance with platform specifications.

Overall, Ad Creative is an essential tool for anyone involved in digital marketing who needs to generate high-quality ad copy efficiently while maintaining alignment with brand messaging and compliance requirements.

When to use it

Use this skill when you need to generate ad copy for various platforms or when you want to optimize existing ads based on performance metrics.

When not to use it

This skill is not suitable for creating landing page copy or for campaign strategy and targeting, which are outside its scope.

What you can build with it

Generating New Ad Campaigns

Use the skill to create fresh ad copy for a new product launch, ensuring alignment with target audience insights.

Optimizing Existing Ads

Analyze performance data from current campaigns to generate new variations that improve click-through and conversion rates.

Scaling Ad Production

Produce large batches of static ad concepts based on a library of successful ads and customer feedback.

How to install Ad Creative

View source

1. Install with the skills CLI

npx skills add coreyhaines31/marketingskills/ad-creative --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by coreyhaines31

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 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, see the daily-creative-drop loop in 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)

ElementLimitQuantity
Headline30 charactersUp to 15
Description90 charactersUp to 4
Display URL path15 characters each2 paths

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)

ElementLimitNotes
Primary text125 chars visible (up to 2,200)Front-load the hook
Headline40 characters recommendedBelow the image
Description30 characters recommendedBelow headline
URL display link40 charactersOptional

LinkedIn Ads

ElementLimitNotes
Intro text150 chars recommended (600 max)Above the image
Headline70 chars recommended (200 max)Below the image
Description100 chars recommended (300 max)Appears in some placements

TikTok Ads

ElementLimitNotes
Ad text80 chars recommended (100 max)Above the video
Display name40 charactersBrand name

Twitter/X Ads

ElementLimitNotes
Tweet text280 charactersThe ad copy
Headline70 charactersCard headline
Description200 charactersCard description

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 iOS-native reveal video ads — iMessage chat reveals (scripted thread unfolds bubble-by-bubble: screenshot hook → friend asks "what app is that?" → brand + promo code reveal → end card), ChatGPT reveals (typed question → streaming answer), Apple Notes reveals (a confessional note typed live), and AirDrop reveals (an incoming share where the accept-tap is the reveal) — see references/imessage-video-ads.md for surface selection, 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 (strictest for fabricated AI answers).

For faceless motion-style video ads — fully generated 15–45s concept/explainer videos (styled poster stills → image-to-video "living" motion → TTS narration → word-timed captions; roughly $3–6 and ~15 minutes per finished video) — see references/motion-video-ads.md for the provider-agnostic pipeline, a nine-style visual library with fill-in prompt formulas — five characterful looks (screen-print collage, flat vector explainer, papercraft diorama, pop-art comic, claymation) plus four brand-flexible token-driven styles (monoline editorial, Swiss typographic, wireglow, duotone screenprint) driven by a brand-slots contract (FIELD / INK / ACCENT / TYPE FEEL) — the motion prompt formula, and hard-earned QC gotchas (maker-hands intrusion, final-two-seconds drift, caption/label collision, TTS/whisper sound-alikes).

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:

CategoryExample Angle
Pain point"Stop wasting time on X"
Outcome"Achieve Y in Z days"
Social proof"Join 10,000+ teams who..."
Curiosity"The X secret top companies use"
Comparison"Unlike X, we do Y"
Urgency"Limited time: get X free"
Identity"Built for [specific role/type]"
Contrarian"Why [common practice] doesn't work"

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.

Creative Review Page (client / stakeholder approval)

When a person who isn't you needs to review and pick — a client, a partner, a stakeholder — produce a creative review page: a self-contained HTML artifact that presents each concept as an in-feed platform mockup (Instagram/Facebook, with a whitelist-handle toggle), breaks carousels into a labeled frame-by-frame storyboard, lets them toggle headline/copy variations, and discloses what's grounded in real assets. It's the visual upgrade to INDEX.md — a decision made off one link instead of by reading markdown. The template ships at assets/creative-review-template.html (one file, no build, hostable anywhere); populate its DATA object from your generated concepts. Full data model, grounding rules (the disclosure block is required), and delivery in references/creative-review-page.md.

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.

PlatformPull Performance DataManage CampaignsGuide
Google Adsgoogle-ads campaigns list, google-ads reports getgoogle-ads campaigns creategoogle-ads.md
Meta Adsmeta-ads insights getmeta-ads campaigns listmeta-ads.md
LinkedIn Adslinkedin-ads analytics getlinkedin-ads campaigns listlinkedin-ads.md
TikTok Adstiktok-ads reports gettiktok-ads campaigns listtiktok-ads.md

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

Frequently asked questions about Ad Creative

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