Agent skill · Content & Marketing

competitor-ad-intelligence

Scrape competitor ads from Meta Ad Library and Google Ads Transparency Center, analyze creative patterns (hooks, formats, CTAs), reverse-engineer landing page funnels, and produce a strategic teardown with vulnerability analysis and counter-play recommendations. Use when you need to understand the competitive ad landscape, find new creative directions, or identify weaknesses in a competitor's paid strategy.

gooseworks-aigithub.com/gooseworks-aiGitHub ↗
claude-codecodexcursorMIT
Install
npx skills add gooseworks-ai/goose-skills --skill competitor-ad-intelligence --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 12 KB
Bundled scripts: none
Path: skills/ads/composites/competitor-ad-intelligence/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 1,091
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Competitor Ad Intelligence Scrape competitor ads from Meta and Google, analyze creative patterns, reverse-engineer landing page funnels, and produce a full strategic teardown — hooks, formats, positioning bets, vulnerabilities, and counter-plays. **Core principle:** A competitor's ad portfolio is a window into their growth strategy. Long-running ads reveal what converts. New ads reveal what they're testing. Landing pages reveal their positioning bets. The best ad creative teams start with evidence from what's already working, then differentiate. ## When to Use - "What ads are my competitors running?" - "Tear down [competitor]'s ad strategy" - "Find new creative angles for our paid campaigns" - "Reverse-engineer [competitor]'s paid funnel" - "What hooks are working in [our space]?" - "Audit the ad landscape before we launch" - "Find weaknesses in [competitor]'s ad strategy" - "What format — video, image, carousel — is dominant in our category?" ## Phase 0: Intake Gather from the user: 1. **Competitor names + domains** (e.g., `apollo.io`, `clay.run`) 2. **Your product/domain** — for comparison framing 3. **Channels:** Meta only, Google only, or both? (default: both) 4. **Depth leve

What's inside
Steps it walks through
  1. When to Use
  2. Phase 0: Intake
  3. Phase 1: Scrape Meta Ads
  4. Phase 2: Scrape Google Ads
  5. Phase 3: Analyze Creative Patterns
  6. Hook Pattern Clustering
  7. Format Distribution
  8. CTA Taxonomy
  9. Phase 4: Landing Page & Funnel Analysis
  10. Campaign Clustering
  11. Per-Campaign Funnel Analysis
  12. Budget Allocation Inference
  13. Phase 5: Strategic Analysis
  14. Creative Gap Analysis
Ships with 1 file
  • skill.meta.json
More from goose-skills
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About this skill
What does the competitor-ad-intelligence skill do?

Scrape competitor ads from Meta Ad Library and Google Ads Transparency Center, analyze creative patterns (hooks, formats, CTAs), reverse-engineer landing page funnels, and produce a strategic teardown with vulnerability analysis and counter-play recommendations. Use when you need to understand the competitive ad landscape, find new creative directions, or identify weaknesses in a competitor's paid strategy.

How do I install it?

Run `npx skills add gooseworks-ai/goose-skills --skill competitor-ad-intelligence --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From gooseworks-ai/goose-skills, a repository with 1,091 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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