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competitive-ads-extractor

Extracts and analyzes competitors' ads from ad libraries (Facebook, LinkedIn, etc.) to understand what messaging, problems, and creative approaches are working. Helps inspire and improve your own ad campaigns.

57

1.22x
Quality

36%

Does it follow best practices?

Impact

97%

1.22x

Average score across 3 eval scenarios

SecuritybySnyk

Advisory

Suggest reviewing before use

Optimize this skill with Tessl

npx tessl skill review --optimize ./.trae/skills/competitive-ads-extractor/SKILL.md
SKILL.md
Quality
Evals
Security

Evaluation results

93%

20%

ClearView CRM Competitive Ad Analysis

Analysis report structure and output formats

Criteria
Without context
With context

Analysis markdown file

100%

100%

Overview: total ad count

100%

100%

Overview: themes with percentages

25%

50%

Overview: format split

75%

100%

Overview: CTA patterns

100%

100%

Key Problems section

50%

100%

Key Problems: copy examples

62%

100%

Creative Patterns section

90%

100%

Copy section: headlines

75%

62%

Copy section: body patterns

100%

100%

Audience Targeting section

25%

100%

Recommendations section

80%

100%

CSV output

100%

100%

100%

24%

First Paid Ad Campaign Planning for Focusly

Ad campaign planning workflow

Criteria
Without context
With context

Competitor patterns identified

70%

100%

Key problems highlighted

70%

100%

Messaging gaps identified

91%

100%

Unique angles brainstormed

83%

100%

Test ad variations drafted

93%

100%

Copy patterns documented

37%

100%

Audience segments mapped

87%

100%

CTA patterns noted

85%

100%

Recommendations section

40%

100%

CSV output

87%

100%

100%

12%

Market Entry Positioning Strategy for GridHR

Competitive positioning research workflow

Criteria
Without context
With context

All competitors analyzed

100%

100%

Positioning mapped per competitor

100%

100%

Key problems per competitor

100%

100%

Audience segments per competitor

75%

100%

Cross-competitor common themes

90%

100%

Underserved angles identified

93%

100%

Differentiated messaging developed

100%

100%

CTA patterns across competitors

28%

100%

Recommendations section

80%

100%

Structured comparison output

80%

100%

Repository
Lingjie-chen/MT5
Evaluated
Agent
Claude Code
Model
Claude Sonnet 4.6

Table of Contents

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