AI-consumed reference. Optimized for Claude to read during execution. Human-readable explanation: see docs/architecture/HIERARCHICAL_PLANNING.md or docs/getting-started/ depending on topic.
Learning Analyzer Skill
Analyze learning data from Supabase: success/failure patterns, optimization opportunities, agent performance.
Usage
/af learn analyze # Full analysis
/af learn analyze --period 30d # Last 30 days
/af learn analyze --focus agents # Agent performance
/af learn analyze --focus workflows # Workflow patterns
/af learn analyze --focus feedback # User feedback
Process
1. Query Supabase Views
views[5]{view,purpose}:
v_agent_success_rates,Agent performance by task type
v_common_patterns,Identified patterns
v_improvement_suggestions,Actionable suggestions
v_workflow_trends,Weekly workflow trends
v_feedback_summary,Feedback statistics
2. AI Pattern Recognition
Identify: Top 3 success patterns, top 3 failure patterns, top 3 optimization opportunities, agent recommendations.
3. Output Report
## Learning Analysis Report
Generated: {timestamp} | Period: {dates}
### Success Patterns
1. **Pattern:** {description} — Frequency: {N}, Confidence: {%}
### Failure Patterns
1. **Pattern:** {description} — Impact: {severity}, Suggested Fix: {fix}
### Optimization Opportunities
1. **Opportunity:** {description} — Savings: {tokens/time}
### Agent Recommendations
| Task Type | Agent | Success Rate | Confidence |
### Suggested Rule Updates
- [ ] {suggestion}
Environment
AF_LEARNING_ENABLED=true
SUPABASE_URL=https://your-project.supabase.co
SUPABASE_SERVICE_KEY=your-service-role-key
Integration
After analysis, improvements can be: reviewed (/af learn review), auto-applied (/af learn apply --auto, high confidence only), or saved as pending (/af learn save).
Source: nguyenthienthanh/aura-frog — distributed by TomeVault.