learning-analyzer
Analyze collected learning data from Supabase to identify success patterns, failure patterns, optimization opportunities, and agent performance trends.
Best use case
learning-analyzer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Analyze collected learning data from Supabase to identify success patterns, failure patterns, optimization opportunities, and agent performance trends.
Teams using learning-analyzer should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/learning-analyzer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How learning-analyzer Compares
| Feature / Agent | learning-analyzer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Analyze collected learning data from Supabase to identify success patterns, failure patterns, optimization opportunities, and agent performance trends.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
> **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.
# Learning Analyzer Skill
Analyze learning data from Supabase: success/failure patterns, optimization opportunities, agent performance.
---
## 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
```
---
## 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}
```
---
## Environment
```bash
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`).
---