القائمة
Analyze collected learning data from Supabase to identify success patterns, failure patterns, optimization opportunities, and agent performance trends.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
CRITICAL: MUST run for EVERY message. Detects agent, complexity, AND model automatically. Without this, tasks route to wrong agents and use wrong models, degrading quality and wasting tokens.
5 techniques for different problem types. Use when stuck or facing complex challenges.
Structured thinking process for complex analysis. Supports revision, branching, and dynamic adjustment.
Angular 17+ gotchas and decision criteria. Covers signals vs observables, standalone patterns, and common pitfalls Claude gets wrong.
Designs RESTful APIs with endpoint naming, versioning strategies (URL path, header-based), pagination (offset and cursor), error response schemas, and OpenAPI conventions. Use when the user asks about REST API design, creating endpoints, URL structure, API versioning, status codes, Swagger, or OpenAPI specs.
Fast bug fixes with root cause investigation + TDD. Enforces 'no fix without root cause' discipline and verification protocol. Without this skill, fixes are applied at symptoms instead of sources, and bugs return.
| name | learning-analyzer |
| description | Analyze collected learning data from Supabase to identify success patterns, failure patterns, optimization opportunities, and agent performance trends. |
| autoInvoke | false |
| priority | 30 |
| triggers | ["/learn analyze","learning analysis","pattern analysis"] |
| context | fork |
| user-invocable | false |
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.
Analyze learning data from Supabase: success/failure patterns, optimization opportunities, agent performance.
/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
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
Identify: Top 3 success patterns, top 3 failure patterns, top 3 optimization opportunities, agent recommendations.
## 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}
AF_LEARNING_ENABLED=true
SUPABASE_URL=https://your-project.supabase.co
SUPABASE_SERVICE_KEY=your-service-role-key
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).