Analyze Performance

Analyze Content Performance

Identify patterns in high-performing posts to inform future content strategy.

Process

1. Run ./scripts/print-published.sh linkedin-post to read all published LinkedIn posts
2. Extract posts that have engagement data (engagement.reactions, engagement.views, etc.)
3. Analyze patterns across high-performing vs low-performing posts

Analysis Dimensions

Hook Analysis

• What hook styles correlate with higher engagement?
• Personal anecdote vs company experience vs surprising data vs news hook?
• First 210 characters (LinkedIn cutoff) - what patterns work?

Content Characteristics

• Word count vs engagement correlation
• Use of concrete examples vs abstract concepts
• Presence of frameworks or mental models
• Use of lists/structure vs flowing narrative

Topic Analysis

• Which tags correlate with higher engagement?
• Which themes resonate most?
• Timing patterns (if publishedDate available)

Structural Patterns

• Opening style (question, statement, story)
• Closing style (call-to-action, reflection, question)
• Paragraph length and density

Performance Tiers

Categorize posts by reaction count:

• High performers: 100+ reactions
• Medium performers: 30-99 reactions
• Lower performers: <30 reactions

Output Format

Provide:

1. Summary statistics - Total posts analyzed, average engagement by tier
2. Top performers - List highest-engagement posts with their key characteristics
3. Pattern insights - What distinguishes high vs lower performers?
4. Recommendations - Actionable suggestions for future content

Example Analysis Output

## Performance Summary
- Posts analyzed: 12 (with engagement data)
- High performers (100+): 3 posts
- Medium performers (30-99): 5 posts
- Lower performers (<30): 4 posts

## Top Performers
1. "Title" - 245 reactions
   - Hook: Personal anecdote
   - Topic: AI productivity
   - Word count: 180

## Key Patterns
- Personal anecdotes in the first sentence correlate with 2x higher engagement
- Posts with concrete examples outperform abstract posts by 40%
- Optimal word count appears to be 150-200 words

## Recommendations
1. Lead with personal or company-specific openings
2. Include at least one specific example or data point
3. Keep total length under 220 words

Notes

• Only analyze posts with engagement data (skip posts without metrics)
• Correlation is not causation - note patterns but don't overclaim
• Consider recency bias - newer posts may still be accumulating engagement

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: techwolf-ai
• Source: techwolf-ai/ai-first-toolkit
• License: MIT
• Homepage: https://ai-first.techwolf.ai

Install and usage instructions live in the source repository linked above.