self-improve · diff
git:20260515.6be9f59 to git:20260819.03ddfe3
84 added, 4 removed. Audit A to A.
---
name: self-improve
- description: "Apply learned improvements to the Aura Frog plugin. Updates rules, adjusts agent routing, modifies workflow configurations, and generates knowledge base entries."
+ description: "Full learning loop for the Aura Frog plugin: analyze collected learning data from Supabase to identify success patterns, failure patterns, optimization opportunities, and agent performance trends, then apply learned improvements — update rules, adjust agent routing, modify workflow configurations, and generate knowledge base entries."
autoInvoke: false
priority: 30
triggers:
+ - "/learn analyze"
- "/learn apply"
+ - "/af learn analyze"
- "self improve"
- "apply improvements"
+ - "analyze learning data"
+ - "success patterns"
+ - "failure patterns"
+ - "agent performance trends"
+ - "optimization opportunities"
+ - "learning analysis"
+ - "pattern analysis"
user-invocable: false
---
> **AI-consumed reference.** Optimized for Claude to read during execution.
> Human-readable explanation: see [docs/architecture/HIERARCHICAL_PLANNING.md](../../../docs/architecture/HIERARCHICAL_PLANNING.md)
> or [docs/getting-started/](../../../docs/getting-started/) depending on topic.
# Self-Improve Skill
- Apply learned improvements: update rules, adjust agent routing, modify workflow configs, generate knowledge entries.
+ 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).
---
- ## Usage
+ ## 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
+
```bash
+ /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
+
+ ```toon
+ 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
+
+ ```markdown
+ ## 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
+
+ ```bash
+ 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
+
+ ```bash
/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
```toon
types[4]{type,target,example}:
Rule updates,rules/*.md,Increase coverage threshold 80→85
- Agent routing,agent-detector config,Default react-expert for .tsx
+ 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
1. **Fetch:** Query `v_improvement_suggestions WHERE applied = FALSE`
2. **Generate:** Determine target files, create modifications, calculate impact
3. **Review:** Present diff with confidence, frequency, evidence. User chooses: Apply / Skip / Modify
4. **Apply:** Create backup, apply modification, mark applied in Supabase, log change
---
## Rollback
```bash
/af learn rollback <change_id> # Specific change
/af learn rollback --list # List recent changes
/af learn rollback --all # All changes from today
```
---
## Configuration
```yaml
learning:
self_improve:
enabled: true
auto_apply_threshold: 0.8
min_frequency: 5
backup_dir: backups/
max_auto_per_day: 10
```
---