ad-library-teardown

name	ad-library-teardown
description	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.
allowed-tools	Bash, Read, Write, WebFetch
version	1.0.0
author	ScrapeCreators
license	MIT
homepage	https://scrapecreators.com
repository	https://github.com/ScrapeCreators/social-media-research-skills
metadata	{"openclaw":{"requires":{"env":["SCRAPECREATORS_API_KEY"]},"primaryEnv":"SCRAPECREATORS_API_KEY","homepage":"https://scrapecreators.com","tags":["social-media","research","scrapecreators"]}}

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 library	Search/list endpoint	Detail endpoint	Transcript 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/ad	n/a
LinkedIn	/v1/linkedin/ads/search	/v1/linkedin/ad	n/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.