nguyenthienthanh 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.
Self-Improve Skill
Full learning loop: Analyze collected learning data (success/failure patterns, optimization opportunities, agent performance) → Apply learned improvements (update rules, adjust agent routing, modify workflow configs, generate knowledge entries).
Analyze
Analyze learning data from Supabase: success/failure patterns, optimization opportunities, agent performance. Runs before Apply — its v_improvement_suggestions output feeds the Apply process below.
Analyze 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
Analyze 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}
Analyze Environment
AF_LEARNING_ENABLED=true
SUPABASE_URL=https://your-project.supabase.co
SUPABASE_SERVICE_KEY=your-service-role-key
Analyze → Apply Handoff
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). The Apply section below consumes these suggestions.
Apply
Apply learned improvements: update rules, adjust agent routing, modify workflow configs, generate knowledge entries.
Apply Usage
/af learn apply # Review and apply pending
/af learn apply --auto # Auto-apply high-confidence (>=0.8)
/af learn apply --preview # Preview without applying
/af learn apply --id <pattern_id> # Apply specific pattern
Improvement Types
types[4]{type,target,example}:
Rule updates,rules/*.md,Increase coverage threshold 80→85
Agent routing,agent-detector config,Load framework-expert refs/react.md for .tsx
Workflow adjustments,workflow config,Increase Phase 2 timeout
Knowledge base,knowledge entries,TDD reduces bugs by 40% for APIs
Safety Guards
Approval required unless: --auto AND confidence >= 0.8 AND frequency >= 5.
Rollback: Every change creates backup + log. /af learn rollback <id> or --all.
Validation: Syntax check, conflict detection, impact assessment before applying.
Apply Process
- Fetch: Query
v_improvement_suggestions WHERE applied = FALSE - Generate: Determine target files, create modifications, calculate impact
- Review: Present diff with confidence, frequency, evidence. User chooses: Apply / Skip / Modify
- Apply: Create backup, apply modification, mark applied in Supabase, log change
Rollback
/af learn rollback <change_id> # Specific change
/af learn rollback --list # List recent changes
/af learn rollback --all # All changes from today
Configuration
learning:
self_improve:
enabled: true
auto_apply_threshold: 0.8
min_frequency: 5
backup_dir: backups/
max_auto_per_day: 10