competitive-ads-extractor
Extracts and analyzes competitors' ads from ad libraries (Facebook, LinkedIn, etc.) to understand what messaging, problems, and creative approaches are working.
Skill by CommandCodeAI
Chalk Labs
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Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/commandcodeai-competitive-ads-extractor
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: marketing
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What's inside
- Skill name
competitive-ads-extractor- Description
- 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.
- License
- MIT
- Instructions
- 290 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# Competitive Ads Extractor This skill extracts your competitors' ads from ad libraries and analyzes what's working—the problems they're highlighting, use cases they're targeting, and copy/creative that's resonating. ## When to Use This Skill - Researching competitor ad strategies - Finding inspiration for your own ads - Understanding market positioning - Identifying successful ad patterns - Analyzing messaging that works - Discovering new use cases or pain points - Planning ad campaigns with proven concepts ## What This Skill Does 1. **Extracts Ads**: Scrapes ads from Facebook Ad Library, LinkedIn, etc. 2. **Captures Screenshots**: Saves visual copies of all ads 3. **Analyzes Messaging**: Identifies problems, use cases, and value props 4. **Categorizes Ads**: Groups by theme, audience, or format 5. **Identifies Patterns**: Finds common successful approaches 6. **Provides Insights**: Explains why certain ads likely perform well ## How to Use ### Basic Extraction ``` Extract all current ads from [Competitor Name] on Facebook Ad Library ``` ``` Scrape ads from [Company] and analyze their messaging ``` ### Specific Analysis ``` Get all ads from [Competitor] focusing on their messaging about [specific problem]. What pain points are they highlighting? ``` ### Competitive Set ``` Extract ads from these 5 competitors: [list]. Compare their approaches and tell me what's working. ``` ### Specific Platform ``` Get LinkedIn ads from [Competitor] and analyze their B2B positioning strategy ``` ## Example **User**: "Extract ads from Notion on Facebook Ad Library and tell me what messaging is working for them." **Process**: ``` Accessing Facebook Ad Library... Searching for: Notion Found: 23 active ads Extracting screenshots... [████████████████████] 100% Saved to: competitor-ads/notion/ - ad-001-collaboration.png - ad-002-productivity.png - ad-003-templates.png ... Analyzing messaging... ``` **Output**: ``` # Notion Ad Analysis ## Overview - Total Ads: 23 active - Primary Themes: Productivity (35%), Collaboration (30%), Templates (20%), AI Features (15%) - Ad Formats: Static images (60%), Video (40%) - CTA Patterns: "Try for free", "Get started" ## Key Problems They're Highlighting 1. **Scattered Information** (8 ads) Copy: "Stop switching between 10 different tools" Why it works: Direct pain point many teams face 2. **Meeting Overload** (5 ads) Copy: "Replace unnecessary meetings with async updates" Why it works: Post-COVID remote work pain point 3. **Lost Documentation** (4 ads) Copy: "Never ask 'where is that doc?' again" Why it works: Universal workplace frustration ## Successful Creative Patterns ### Pattern 1: Before/After Split - Shows chaotic tool landscape → Clean Notion workspace - Used in 6 high-performing ads - Visual metaphor is immediately clear ### Pattern 2: Feature Showcase - GIF of actual product usage - Shows specific feature in 5 seconds - Used for new features (AI, templates) ### Pattern 3: Social Proof - "Join 20M users" messaging - Customer logos - Used in 4 ads targeting enterprise
From CommandCodeAI/agent-skills, MIT.
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Signals
- Stars
- 137
- Forks
- 42
- Open issues
- 4
- Last commit (this folder)
- Dec 24, 2025 (10 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/CommandCodeAI/agent-skills
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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competitive-ads-extractor skill. Agent Mart. https://agentmart.dev/skills/commandcodeai-competitive-ads-extractor (last updated 2026-10-24)