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