| 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
- Run
./scripts/print-published.sh linkedin-post to read all published LinkedIn posts
- Extract posts that have engagement data (engagement.reactions, engagement.views, etc.)
- 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:
- Summary statistics - Total posts analyzed, average engagement by tier
- Top performers - List highest-engagement posts with their key characteristics
- Pattern insights - What distinguishes high vs lower performers?
- 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
