Agent skill
Growth & Learning
Analytics, feedback processing, and continuous improvement
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/tier4-growth
SKILL.md
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)
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