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ad-library-teardown

Analyze public ads from Meta/Facebook, Google, and LinkedIn to understand competitor messaging and offers. Extract hooks, headlines, and calls to action, then suggest advertising tests grounded in recurring patterns and gaps.

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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.

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适用场景

  • Research competitors’ active ads
  • Compare messaging and offers across brands
  • Extract hooks and key messages from video ads
  • Develop test ideas for your own ads

包含的文件(1)

  • SKILL.md3.7 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Ad Library Teardown

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.

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