Ad Library Teardown

SkillWeb & browsing

Lets your agent analyze public ads from Meta, Google, or LinkedIn to extract messaging, hooks, offers, and test ideas.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Ad Library Teardown skill

About this capability

Use when the user wants to analyze active ads from Meta/Facebook, Google, or LinkedIn ad libraries; tear down a competitor's messaging; extract hooks, offers, CTAs, video transcripts, landing page claims, and test ideas from public ads.

What this skill tells your AI

The instructions your AI receives, as published by scrapecreators/social-media-research-skills in skills/ad-library-teardown/SKILL.md and read by ahel’s review.

Overview

Analyze public ads to understand a competitor's messaging, offers, creative strategy, and testing angles. The output should be a practical teardown marketers can use to write better ads or decide what to test.

When to Use

Use this skill when the user asks to:

  • analyze a competitor's active ads
  • search Meta/Facebook, Google, or LinkedIn ad libraries
  • extract ad hooks, CTAs, claims, offers, and landing page angles
  • compare ad messaging across competitors
  • summarize video ad transcripts
  • generate ad test ideas from competitor ads

Data Sources

Ad librarySearch/list endpointDetail endpointTranscript endpoint
Meta/Facebook/v1/facebook/adLibrary/search/ads, /v1/facebook/adLibrary/company/ads, /v1/facebook/adLibrary/search/companies/v1/facebook/adLibrary/ad/v1/facebook/adLibrary/ad/transcript
Google/v1/google/adLibrary/advertisers/search, /v1/google/company/ads/v1/google/adn/a
LinkedIn/v1/linkedin/ads/search/v1/linkedin/adn/a

Workflow

  1. Find the advertiser

    • Use company search endpoints when the user provides only a brand name.
    • Use domain/advertiser/page IDs when available.
  2. Fetch active ads

    • Prefer active ads unless the user asks for historical analysis.
    • Capture platform, advertiser/page, ad ID, start date, creative type, text, headline, CTA, destination URL, and source URL.
  3. Fetch details for representative ads

    • Enrich the ads with detail endpoints.
    • For video Meta ads, fetch transcripts when available.
  4. Cluster messaging Group ads by:

    • pain point
    • persona
    • offer
    • proof/social proof
    • feature/benefit
    • objection handled
    • comparison/alternative angle
    • urgency/discount
  5. Extract swipeable elements

    • hooks
    • headlines
    • primary text patterns
    • CTAs
    • claims
    • offers
    • visual/creative concepts
  6. Recommend tests Suggest tests based on repeated patterns and gaps, not random ideas.

Output Format

# Ad Library Teardown: {brand}

## Summary
- Ads analyzed: {count}
- Platforms: Meta / Google / LinkedIn
- Main positioning:
- Strongest repeated offer:

## Messaging Angles
| Angle | Evidence | Example ads | Notes |
|---|---|---|---|

## Hooks and Headlines Swipe File
- "..."
- "..."

## Offers and CTAs
| Offer | CTA | Platform | Example |
|---|---|---|---|

## Video Transcript Notes
- [Ad](url): summary, hook, best quote

## What They Appear to Be Testing
1. ...
2. ...

## Recommended Tests for Us
1. ...
2. ...
3. ...

## Sources
- [Ad](url)

Common Pitfalls

  • Do not claim an ad is winning just because it is active. Say it is active or repeated; performance is not public unless the endpoint returns it.
  • Do not ignore repeated ads. Repetition is often a useful signal.
  • Do not invent spend, conversion rate, or targeting unless public data includes it.
  • Do not skip video transcripts when the user asks for hooks or messaging from video ads.

Signals

GitHub stars
2k
Forks
27
Last commit
Aug 2026
Advanced
Catalog kind
skill
Gateway key
ad-library-teardown
Source
github.com/scrapecreators/social-media-research-skills