/analytics

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 ${CLAUDE_PLUGIN_ROOT}/lib/learning_analytics.py show --dir .claude-patterns

Output Format

The command produces a comprehensive terminal dashboard with:

1. Overview Section: Total patterns, quality scores, success rates
2. Quality Trend Chart: ASCII visualization of quality progression over time
3. Learning Velocity: Improvement rates and trajectory analysis
4. Top Performing Skills: Rankings by success rate and quality contribution
5. Top Performing Agents: Rankings by reliability and efficiency
6. Skill Synergies: Best skill combinations discovered
7. Prediction System Status: Accuracy and model training metrics
8. Cross-Project Learning: Universal pattern statistics
9. Learning Patterns: Fastest and slowest learning areas
10. 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 ${CLAUDE_PLUGIN_ROOT}/lib/learning_analytics.py export-json --output data/reports/analytics.json --dir .claude-patterns

Export as Markdown

# Auto-detects plugin path
python ${CLAUDE_PLUGIN_ROOT}/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 ${CLAUDE_PLUGIN_ROOT}/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

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