growth-learning
Analytics, feedback processing, and continuous improvement
When & Why to Use This Skill
This Claude skill facilitates a comprehensive 'Growth & Learning' feedback loop designed for continuous system optimization. By integrating performance analytics, audience feedback processing, and automated code reviews, it enables users to transform raw session data into a structured knowledge base of lessons learned and evolving best practices.
Use Cases
- Performance Benchmarking: Automatically evaluate YouTube engagement and retention metrics against industry-standard benchmarks to identify specific growth opportunities.
- Audience Sentiment Analysis: Process viewer comments and feedback to extract qualitative insights and integrate 'Voice of the Customer' into future project planning.
- Iterative System Improvement: Maintain an evolving knowledge base of lessons learned to ensure successful patterns are replicated and known pitfalls are avoided in subsequent sessions.
- Technical Quality Assurance: Conduct automated code quality reviews to ensure development standards are met and continuously improved over time.
| name | Growth & Learning |
|---|---|
| tier | 4 |
| load_policy | conditional |
| description | Analytics, feedback processing, and continuous improvement |
| version | 1.0.0 |
Growth & Learning Skill (Tier 4)
The Feedback Loop That Makes Us Better
This tier contains skills for analyzing performance and improving future sessions.
Purpose
Process feedback, analyze metrics, and continuously improve the Dreamweaving system.
Load Conditions
This tier loads conditionally when:
- Processing YouTube analytics data
- Analyzing viewer comments
- Reviewing code quality
- Applying lessons to new sessions
Sub-Skills
| Skill | Location | Purpose |
|---|---|---|
| Analytics Learning | analytics-learning/ |
Process YouTube metrics |
| Feedback Integration | feedback-integration/ |
Extract insights from comments |
Feedback Loop
Session Published
↓
Analytics Collected (30-90 days)
↓
Lessons Extracted
↓
Knowledge Base Updated
↓
Applied to Next Session
↓
[Repeat]
Key Metrics Tracked
| Category | Metrics |
|---|---|
| Engagement | Views, likes, comments, shares |
| Retention | Average view duration, retention % |
| Growth | Subscribers gained, impressions |
| Quality | Like ratio, comment sentiment |
Benchmarks
| Metric | Good | Average | Needs Work |
|---|---|---|---|
| Retention (30 min) | >50% | 30-50% | <30% |
| Like Ratio | >8% | 4-8% | <4% |
| Comment Rate | >1% | 0.5-1% | <0.5% |
| Sub Conversion | >2% | 1-2% | <1% |
Knowledge Base Files
| File | Purpose |
|---|---|
knowledge/lessons_learned.yaml |
Accumulated insights |
knowledge/best_practices.md |
Evolving standards |
knowledge/analytics_history/ |
Historical data |
Commands
# Process analytics for a session
/learn-analytics session-name
# Analyze comments
/learn-comments session-name
# Review code quality
/review-code
# Show accumulated lessons
/show-lessons
Integration with Production
When creating new sessions:
- Check
lessons_learned.yamlfirst - Apply successful patterns
- Avoid known pitfalls
- Test new approaches systematically
Related Resources
- Serena Memory:
session_learnings_system - Doc:
docs/CANONICAL_WORKFLOW.md(learning section)