Learning Analyzer

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.