analyze-performance

name	analyze-performance
description	Analyze engagement patterns across published posts to identify what works. Use when asked to review performance, find successful patterns, or optimize future content.

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