Ad Creative workflow skill. Use this skill when the user needs Create, iterate, and scale paid ad creative for Google Ads, Meta, LinkedIn, TikTok, and similar platforms. Use when generating headlines, descriptions, primary text, or large sets of ad variations for testing and performance optimization and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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name
ad-creative-v2
description
Ad Creative workflow skill. Use this skill when the user needs Create, iterate, and scale paid ad creative for Google Ads, Meta, LinkedIn, TikTok, and similar platforms. Use when generating headlines, descriptions, primary text, or large sets of ad variations for testing and performance optimization and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages plugins/antigravity-awesome-skills/skills/ad-creative from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.
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.
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Before Starting, How This Skill Works, Platform Specs, Generating Ad Visuals, Generating Ad Copy, Iterating from Performance Data.
When to Use This Skill
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
Use when generating or iterating paid ad copy at scale.
Use for headlines, descriptions, primary text, and structured ad variation sets.
Use when performance data should inform the next round of creative.
Use when the request clearly matches the imported source intent: Create, iterate, and scale paid ad creative for Google Ads, Meta, LinkedIn, TikTok, and similar platforms. Use when generating headlines, descriptions, primary text, or large sets of ad variations for testing and....
Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
Use when provenance needs to stay visible in the answer, PR, or review packet.
Operating Table
Situation
Start here
Why it matters
First-time use
metadata.json
Confirms repository, branch, commit, and imported path through the external_source block before touching the copied workflow
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
references/generative-tools.md
Starts with the smallest copied file that materially changes execution
Supporting context
references/platform-specs.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
Workflow
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
Headline generation — Focused on click-through
Description generation — Focused on conversion
Primary text generation — Focused on engagement (Meta/LinkedIn)
Flag anything that might violate platform policies
Ensure headline/description combinations make sense together
Imported: Before Starting
Check for product marketing context first:
If .agents/product-marketing-context.md exists (or .claude/product-marketing-context.md 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)
Any mandatory elements? (Brand name, trademark symbols, disclaimers)
Examples
Example 1: Ask for the upstream workflow directly
Use @ad-creative-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Example 2: Ask for a provenance-grounded review
Review @ad-creative-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Example 3: Narrow the copied support files before execution
Use @ad-creative-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Example 4: Build a reviewer packet
Review @ad-creative-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
Best Practices
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
Treat generated examples as scaffolding; adapt them to the concrete task before execution.
Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.
Troubleshooting
Problem: The operator skipped the imported context and answered too generically
Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/ad-creative, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.
Problem: The imported workflow feels incomplete during review
Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
Problem: The task drifted into a different specialization
Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better.
Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
Imported Troubleshooting Notes
Imported: 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
Testing too many things at once — Change one variable per test cycle
Retiring creative too early — Allow 1,000+ impressions before judging
Related Skills
@00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
@00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
Additional Resources
Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
Resource family
What it gives the reviewer
Example path
references
copied reference notes, guides, or background material from upstream
references/generative-tools.md
examples
worked examples or reusable prompts copied from upstream
examples/n/a
scripts
upstream helper scripts that change execution or validation
scripts/n/a
agents
routing or delegation notes that are genuinely part of the imported package
agents/n/a
assets
supporting assets or schemas copied from the source package
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
Imported: 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
30 characters
Up to 15
Description
90 characters
Up to 4
Display URL path
15 characters each
2 paths
RSA rules:
Headlines must make sense independently and in any combination
Pin headlines to positions only when necessary (reduces optimization)
For image and video ad creative, use generative AI tools and code-based video rendering. 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
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:
Generate hero creative with AI tools (exploratory, high-quality)
Build Remotion templates based on winning patterns
Batch produce variations with Remotion using data feeds
Iterate — AI for new angles, Remotion for scale
Imported: 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:
Category
Example 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.
Imported: 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?
