216 lines
8.5 KiB
Markdown
216 lines
8.5 KiB
Markdown
---
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name: learn:analytics
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description: Display learning analytics dashboard with pattern progress, skill effectiveness, and trends
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delegates-to: autonomous-agent:orchestrator
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---
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# Learning Analytics Dashboard
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Display comprehensive analytics about the autonomous agent's learning progress, including:
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- **Pattern Learning Progress**: Quality trends, learning velocity, improvement rates
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- **Skill Effectiveness**: Top performing skills, success rates, quality contributions
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- **Agent Performance**: Reliability scores, efficiency ratings, delegation patterns
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- **Skill Synergies**: Best skill combinations and their effectiveness
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- **Prediction System**: Accuracy metrics and model performance
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- **Cross-Project Learning**: Universal patterns and knowledge transfer
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- **Learning Insights**: Actionable recommendations and trend analysis
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## Execution
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Generate and display the learning analytics report:
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```bash
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# Auto-detects plugin path whether in development or installed from marketplace
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python <plugin_path>/lib/learning_analytics.py show --dir .claude-patterns
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```
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## Output Format
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The command produces a comprehensive terminal dashboard with:
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1. **Overview Section**: Total patterns, quality scores, success rates
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2. **Quality Trend Chart**: ASCII visualization of quality progression over time
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3. **Learning Velocity**: Improvement rates and trajectory analysis
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4. **Top Performing Skills**: Rankings by success rate and quality contribution
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5. **Top Performing Agents**: Rankings by reliability and efficiency
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6. **Skill Synergies**: Best skill combinations discovered
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7. **Prediction System Status**: Accuracy and model training metrics
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8. **Cross-Project Learning**: Universal pattern statistics
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9. **Learning Patterns**: Fastest and slowest learning areas
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10. **Key Insights**: Actionable recommendations based on data
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## Example Output
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```
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+===========================================================================+
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| LEARNING ANALYTICS DASHBOARD - ENHANCED SYSTEM v3.0 |
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+===========================================================================+
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📊 OVERVIEW
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---------------------------------------------------------------------------
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Total Patterns Captured: 156
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Overall Quality Score: 88.5/100
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Success Rate: 92.3%
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Recent Quality: 91.2/100 (+2.7)
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Activity (Last 7 days): 12 patterns
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Activity (Last 30 days): 48 patterns
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📈 QUALITY TREND OVER TIME
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---------------------------------------------------------------------------
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95.0 | ██████████|
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| ████████████████|
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| ████████████████████ |
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| ████████████████████ |
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87.5 | ████████████████ |
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| ████████████ |
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| ████████ |
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| ████████ |
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80.0 |████ |
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+------------------------------------------------------+
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106 -> 156
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Trend: IMPROVING
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🚀 LEARNING VELOCITY
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---------------------------------------------------------------------------
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Weeks Analyzed: 8
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Early Average Quality: 85.3/100
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Recent Average Quality: 91.2/100
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Total Improvement: +5.9 points
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Improvement Rate: 0.74 points/week
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Trajectory: ACCELERATING
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Acceleration: +0.52 (speeding up!)
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⭐ TOP PERFORMING SKILLS
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---------------------------------------------------------------------------
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1. code-analysis Success: 94.3% Quality: 18.5
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2. quality-standards Success: 92.1% Quality: 17.8
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3. testing-strategies Success: 89.5% Quality: 16.2
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4. security-patterns Success: 91.0% Quality: 15.9
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5. pattern-learning Success: 88.7% Quality: 15.1
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🤖 TOP PERFORMING AGENTS
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---------------------------------------------------------------------------
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1. code-analyzer Reliability: 96.9% Efficiency: 1.02
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2. quality-controller Reliability: 95.2% Efficiency: 0.98
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3. test-engineer Reliability: 93.5% Efficiency: 0.89
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4. documentation-generator Reliability: 91.8% Efficiency: 0.95
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5. frontend-analyzer Reliability: 90.5% Efficiency: 1.05
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🔗 SKILL SYNERGIES (Top Combinations)
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---------------------------------------------------------------------------
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1. code-analysis + quality-standards Score: 8.5 Uses: 38
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Quality: 92.3 Success: 97.8% [HIGHLY_RECOMMENDED]
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2. code-analysis + security-patterns Score: 7.2 Uses: 28
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Quality: 91.0 Success: 96.4% [HIGHLY_RECOMMENDED]
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🎯 PREDICTION SYSTEM STATUS
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---------------------------------------------------------------------------
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Status: ACTIVE
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Models Trained: 15 skills
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Prediction Accuracy: 87.5%
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[PASS] High accuracy - automated recommendations highly reliable
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🌐 CROSS-PROJECT LEARNING
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---------------------------------------------------------------------------
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Status: ACTIVE
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Universal Patterns: 45
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Avg Transferability: 82.3%
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[PASS] Knowledge transfer active - benefiting from other projects
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💡 KEY INSIGHTS
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---------------------------------------------------------------------------
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[PASS] Learning is accelerating! Quality improving at 0.74 points/week and speeding up
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[PASS] Recent performance (91.2) significantly better than historical average (88.5)
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[PASS] Highly effective skill pair discovered: code-analysis + quality-standards (8.5 synergy score)
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[PASS] Prediction system highly accurate (87.5%) - trust automated recommendations
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[PASS] Fastest learning in: refactoring, bug-fix
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+===========================================================================+
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| Generated: 2025-10-23T14:30:52.123456 |
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+===========================================================================+
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```
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## Export Options
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### Export as JSON
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```bash
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# Auto-detects plugin path
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python <plugin_path>/lib/learning_analytics.py export-json --output data/reports/analytics.json --dir .claude-patterns
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```
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### Export as Markdown
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```bash
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# Auto-detects plugin path
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python <plugin_path>/lib/learning_analytics.py export-md --output data/reports/analytics.md --dir .claude-patterns
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```
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## Usage Scenarios
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### Daily Standup
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Review learning progress and identify areas needing attention:
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```bash
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/learning-analytics
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```
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### Weekly Review
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Export comprehensive report for documentation:
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```bash
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# Auto-detects plugin path
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python <plugin_path>/lib/learning_analytics.py export-md --output weekly_analytics.md
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```
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### Performance Investigation
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Analyze why quality might be declining or improving:
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```bash
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/learning-analytics
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# Review Learning Velocity and Learning Patterns sections
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```
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### Skill Selection Validation
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Verify which skills and combinations work best:
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```bash
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/learning-analytics
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# Review Top Performing Skills and Skill Synergies sections
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```
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## Interpretation Guide
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### Quality Scores
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- **90-100**: Excellent - Optimal performance
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- **80-89**: Good - Meeting standards
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- **70-79**: Acceptable - Some improvement needed
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- **<70**: Needs attention - Review approach
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### Learning Velocity
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- **Accelerating**: System is learning faster over time (optimal)
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- **Linear**: Steady improvement at constant rate (good)
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- **Decelerating**: Improvement slowing down (may need new approaches)
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### Prediction Accuracy
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- **>85%**: High accuracy - Trust automated recommendations
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- **70-85%**: Moderate accuracy - System still learning
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- **<70%**: Low accuracy - Need more training data
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### Skill Synergies
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- **Score >5**: Highly recommended combination
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- **Score 2-5**: Recommended combination
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- **Score <2**: Use with caution
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## Frequency Recommendations
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- **After every 10 patterns**: Quick check of trends
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- **Weekly**: Full review of all sections
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- **Monthly**: Deep analysis with exported reports
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- **After major changes**: Verify impact on learning
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## Notes
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- Analytics require at least 10 patterns for meaningful insights
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- Learning velocity requires 3+ weeks of data
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- Prediction accuracy improves with more training data
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- Cross-project learning activates automatically when enabled
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- All metrics update in real-time as new patterns are captured
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---
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