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LinkedIn Content Analyzer Skill

Analyze your LinkedIn post performance and generate optimized content drafts with data-driven insights.

What It Does

  • Identifies what content resonates with your audience (engagement: reactions + comments + saves + reposts)
  • Detects patterns in topics and posting times that drive performance
  • Analyzes your writing style and voice
  • Generates 3-5 optimized post drafts
  • Provides timing recommendations for maximum engagement

Setup

Create linkedin-data/ directory in your project root with:

linkedin-data/
├── post_<id>.md              # Individual post analytics
├── post_<id>.md
├── inspiration-topics.md     # (Optional) Future content ideas
└── profile/                  # (Optional) Your background info

Post Analytics Format

Each post_<id>.md should include:

# Post ID: 12345

Date Posted: 2026-03-20
Time Posted: 9:00 AM

## Metrics

- Impressions: 5,420
- Reactions: 320
- Comments: 45
- Saves: 28
- Reposts: 12

## Content

[Your original post text here]

## Audience Demographics

- Top engagement: Specific roles/industries
- Geographic focus: Regions

Inspiration Topics Format

Create linkedin-data/inspiration-topics.md:

# Topics

- Building in Web3
- Product management lessons
- Career transitions
- Industry trends

How to Use

  1. Create linkedin-data/ with 10+ post analytics files
  2. (Optional) Add inspiration-topics.md with content ideas
  3. Invoke skill:
/linkedin-content-analyzer

What You Get

  • 3-5 post drafts with specific angles
  • Optimal posting times (day and hour)
  • Performance analysis (what works, what doesn't)
  • Audience segment insights
  • Updated inspiration topics

Example Output

Draft 1: "Building Infrastructure That Scales"
- Timing: Tuesday, 9 AM ET
- Tone: Technical, actionable
- Predicted engagement: High
- Target: Engineers and founders

[... 4 more drafts ...]

Tips

  • Include metrics from native LinkedIn analytics
  • Keep posts diverse (mix of topics, lengths, styles)
  • Update inspiration topics regularly
  • Run analyzer weekly for trending content detection
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