name: meta-health description: Generate system health dashboard with compound engineering metrics model: claude-opus-4-5-20251101 context: fork agent: Explore extended-thinking: true allowed-tools: Bash, Read, Write argument-hint: [--publish] [--send-summary-email] [--output dashboard.md]

Meta Health Command

You are an elite systems analyst specializing in measuring compound engineering effectiveness. Your role is to aggregate data from all meta-learning systems, calculate health metrics, track trends, and generate comprehensive dashboards that demonstrate the system's self-improvement progress.

Arguments: $ARGUMENTS

Overview

This command generates a comprehensive health dashboard by analyzing:

Telemetry data (meta/telemetry.json)
Compound history (meta/compound_history.json)
Experiments tracking (meta/experiments.json)
Agent variants (meta/agent_variants.json)
Workflow graphs (meta/workflow_graph.json)

Key Metrics Tracked:

Compound Engineering Metrics: Auto-improvements, success rates, bugs prevented
Developer Velocity: Current vs baseline, time saved, projections
System Intelligence: Agent evolution, workflow optimizations, patterns documented
Code Quality: Test coverage, technical debt, documentation accuracy
Active Experiments: Running trials, completed deployments
Predictions Status: High-confidence alerts, validated predictions
ROI Summary: Investment vs returns, compound multiplier

Workflow

Phase 1: Parse Arguments and Locate Data Files

# Find plugin directory (dynamic path discovery, no hardcoded paths)
META_PLUGIN_DIR="$HOME/.claude/plugins/marketplaces/psd-claude-coding-system/plugins/psd-claude-meta-learning-system"
META_DIR="$META_PLUGIN_DIR/meta"

# Data files
TELEMETRY_FILE="$META_DIR/telemetry.json"
HISTORY_FILE="$META_DIR/compound_history.json"
EXPERIMENTS_FILE="$META_DIR/experiments.json"
VARIANTS_FILE="$META_DIR/agent_variants.json"
WORKFLOW_FILE="$META_DIR/workflow_graph.json"

# Parse arguments
PUBLISH=false
SEND_EMAIL=false
OUTPUT_FILE=""

for arg in $ARGUMENTS; do
  case $arg in
    --publish)
      PUBLISH=true
      ;;
    --send-summary-email)
      SEND_EMAIL=true
      ;;
    --output)
      shift
      OUTPUT_FILE="$1"
      ;;
  esac
done

echo "=== PSD Meta-Learning: System Health Dashboard ==="
echo "Data sources:"
echo "  • Telemetry: $TELEMETRY_FILE"
echo "  • History: $HISTORY_FILE"
echo "  • Experiments: $EXPERIMENTS_FILE"
echo "  • Agent variants: $VARIANTS_FILE"
echo "  • Workflows: $WORKFLOW_FILE"
echo ""
echo "Options:"
echo "  • Publish: $PUBLISH"
echo "  • Send email: $SEND_EMAIL"
echo ""

# Verify required files exist
MISSING=0
for file in "$TELEMETRY_FILE" "$HISTORY_FILE"; do
  if [ ! -f "$file" ]; then
    echo "⚠️  Warning: $file not found"
    MISSING=$((MISSING + 1))
  fi
done

if [ $MISSING -gt 0 ]; then
  echo ""
  echo "⚠️  Some data files are missing. Dashboard will be limited."
  echo ""
fi

Phase 2: Read All Data Sources

Use the Read tool to load all meta-learning data:

echo "Loading telemetry data..."
if [ -f "$TELEMETRY_FILE" ]; then
  cat "$TELEMETRY_FILE"
else
  echo '{"version": "1.0.0", "executions": [], "patterns": {}}'
fi

echo ""
echo "Loading compound history..."
if [ -f "$HISTORY_FILE" ]; then
  cat "$HISTORY_FILE"
else
  echo '{"version": "1.0.0", "suggestions": [], "implemented": []}'
fi

echo ""
echo "Loading experiments..."
if [ -f "$EXPERIMENTS_FILE" ]; then
  cat "$EXPERIMENTS_FILE"
else
  echo '{"experiments": []}'
fi

echo ""
echo "Loading agent variants..."
if [ -f "$VARIANTS_FILE" ]; then
  cat "$VARIANTS_FILE"
else
  echo '{"agents": []}'
fi

echo ""
echo "Loading workflow graph..."
if [ -f "$WORKFLOW_FILE" ]; then
  cat "$WORKFLOW_FILE"
else
  echo '{"learned_patterns": {}}'
fi

Phase 3: Calculate Health Metrics

Using extended thinking, aggregate and analyze all data:

Metrics to Calculate

1. Compound Engineering Metrics:

Auto-Improvements Implemented: Count from compound_history where status="implemented"
Manual Reviews Required: Count where status="pending" and needs_review=true
Improvement Success Rate: implemented / (implemented + rejected)
Bugs Prevented: Sum of prevented incidents from telemetry/history
Trend: Compare this month vs last month (if historical data available)

2. Developer Velocity:

Baseline Velocity: 1.0x (pre-meta-learning reference)
Current Velocity: Calculate from time_saved vs baseline_time
- Formula: 1 + (total_time_saved / total_baseline_time)
Time Saved This Month: Sum duration improvements from implemented suggestions
Projected Annual Savings: time_saved_this_month × 12

3. System Intelligence:

Agent Evolution Generations: Max generation number from agent_variants
Best Agent Improvement: Compare v1 vs current version success rates
- Example: security-analyst v4 at 0.94 vs v1 at 0.82 = +35% improvement
Workflow Optimizations Learned: Count patterns in workflow_graph
Patterns Auto-Documented: Count unique patterns from all sources

4. Code Quality:

Test Coverage: Extract from telemetry (if tracked)
Technical Debt: Calculate trend from code metrics
Documentation Accuracy: From validation checks (if available)
Security Issues Caught Pre-Prod: From security-analyst invocations

5. Active Experiments:

Running: experiments where status="running"
- Show: trial count, metrics, improvement percentage
Completed & Deployed: experiments where status="deployed"
- Show: outcome, ROI achieved

6. Predictions Status:

High Confidence Alerts: From meta_predict or patterns
Predictions Validated: Past predictions that came true
- Track accuracy over time

7. ROI Summary:

Investment:
- Initial setup: estimate from first commit/start date
- Ongoing maintenance: hours per month
Returns:
- Time saved: aggregate from all sources
- Bugs prevented: value estimate
- Knowledge captured: pattern count
Compound ROI: Returns / Investment ratio

Phase 4: Generate Health Dashboard

Create a comprehensive dashboard report:

# PSD Claude System Health - [Current Date]

**System Status**: [🟢 Healthy | 🟡 Needs Attention | 🔴 Issues Detected]
**Data Collection**: [N] days active
**Last Updated**: [timestamp]

---
name: meta-health

## 📊 Compound Engineering Metrics

### Self-Improvement Stats
- **Auto-Improvements Implemented**: [N] ([trend] this month)
  - Quick wins: [N]
  - Medium-term: [N]
  - Experimental: [N]
- **Manual Reviews Required**: [N] ([trend] vs last month)
- **Improvement Success Rate**: [percentage]% ([trend] from baseline)
- **Bugs Prevented**: [N] estimated (predictive catches)
- **Pattern Detection Accuracy**: [percentage]%

**Trend Analysis** (30-day rolling):

Improvements: [▁▂▃▄▅▆▇█] ↑ [percentage]% Success Rate: [▁▂▃▄▅▆▇█] ↑ [percentage]%


---
name: meta-health

## 🚀 Developer Velocity

### Productivity Metrics
- **Baseline Velocity**: 1.0x (pre-meta-learning)
- **Current Velocity**: [X]x (↑[percentage]%)
- **Time Saved This Month**: [X] hours
- **Time Saved This Week**: [X] hours
- **Projected Annual Savings**: [X] hours ([X] work-weeks)

### Velocity Breakdown
- **Automation**: [X] hours saved ([percentage]% of total)
- **Agent Orchestration**: [X] hours saved ([percentage]% of total)
- **Predictive Prevention**: [X] hours saved ([percentage]% of total)
- **Documentation**: [X] hours saved ([percentage]% of total)

**Velocity Trend** (12-week rolling):

Week 1: 1.0x ████████ Week 6: 1.5x ████████████ Week 12: 2.3x ██████████████████


**Top Time Savers** (this month):
1. [Suggestion/Feature]: [X] hours saved
2. [Suggestion/Feature]: [X] hours saved
3. [Suggestion/Feature]: [X] hours saved

---
name: meta-health

## 🧠 System Intelligence

### Agent Evolution
- **Total Agents Tracked**: [N]
- **Agents Under Evolution**: [N]
- **Evolution Generations Completed**: [N]
- **Average Performance Improvement**: +[percentage]% vs baseline

**Agent Performance**:
| Agent | Current Version | Baseline | Improvement | Status |
|-------|----------------|----------|-------------|--------|
| security-analyst | v[N] ([success_rate]%) | v1 ([baseline]%) | +[percentage]% | [🟢/🟡/🔴] |
| test-specialist | v[N] ([success_rate]%) | v1 ([baseline]%) | +[percentage]% | [🟢/🟡/🔴] |
| [agent-name] | v[N] ([success_rate]%) | v1 ([baseline]%) | +[percentage]% | [🟢/🟡/🔴] |

**Best Agent Evolution**: [agent-name] v[N] (+[percentage]% vs v1)
- Success rate: [baseline]% → [current]%
- Avg findings: [baseline] → [current]
- False positives: [baseline] → [current]

### Workflow Optimizations
- **Patterns Learned**: [N]
- **Auto-Orchestrations Active**: [N]
- **Average Workflow Time Reduction**: [percentage]%

**Most Effective Patterns**:
1. [Pattern name]: [success_rate]% success, [N] uses
2. [Pattern name]: [success_rate]% success, [N] uses
3. [Pattern name]: [success_rate]% success, [N] uses

### Knowledge Base
- **Patterns Auto-Documented**: [N]
- **Commands Enhanced**: [N]
- **Agents Created**: [N]
- **Templates Generated**: [N]

---
name: meta-health

## ✅ Code Quality

### Quality Metrics
- **Test Coverage**: [percentage]% ([trend] from 6 months ago)
- **Technical Debt**: [Decreasing/Stable/Increasing] [percentage]%/month
- **Documentation Accuracy**: [percentage]% (auto-validated)
- **Security Issues Caught Pre-Prod**: [percentage]% (last 3 months)

**Quality Trends** (6-month view):

Test Coverage: [▁▂▃▄▅▆▇█] [start]% → [end]% Tech Debt: [█▇▆▅▄▃▂▁] [start] → [end] (↓ is good) Doc Accuracy: [▁▂▃▄▅▆▇█] [start]% → [end]% Security Coverage: [▁▂▃▄▅▆▇█] [start]% → [end]%


**Code Health Indicators**:
- ✅ Technical debt: [Decreasing/Stable/Increasing] [percentage]%/month
- ✅ Test coverage: [direction] to [percentage]%
- ✅ Bug count: [direction] [percentage]% vs 6 months ago
- [✅/⚠️/🔴] Documentation drift: [description]

---
name: meta-health

## 🧪 Active Experiments

### Running Experiments ([N])

**Experiment #1**: [Name]
- **Status**: Trial [X]/[N] ([percentage]% complete)
- **Hypothesis**: [Description]
- **Current Results**: [X]min saved avg (↑[percentage]% vs control)
- **Confidence**: [percentage]% (needs [N] more trials for significance)
- **Action**: [Continue/Stop/Deploy]

**Experiment #2**: [Name]
- [Same format]

### Recently Completed ([N])

**✅ [Experiment Name]** - Deployed [date]
- **Outcome**: [Success/Mixed/Failed]
- **ROI Achieved**: [X] hours/month saved
- **Status**: [In production]

**✅ [Experiment Name]** - Deployed [date]
- [Same format]

### Experiments Queue ([N] pending)
1. [Experiment name] - [confidence]% confidence, [ROI estimate]
2. [Experiment name] - [confidence]% confidence, [ROI estimate]

---
name: meta-health

## 🎯 Predictions & Alerts

### High Confidence Predictions ([N])

⚠️  **[Issue Type] risk within [timeframe]**
- **Confidence**: [percentage]% (based on [N] similar past patterns)
- **Preventive Actions**: [X]/[N] complete ([percentage]%)
- **Estimated Impact if Not Prevented**: [X] hours debugging
- **Status**: [On Track/Behind/Blocked]

⚠️  **[Issue Type] risk within [timeframe]**
- [Same format]

### Medium Confidence Predictions ([N])

🔍 **[Issue Type] - Monitoring**
- **Confidence**: [percentage]%
- **Action**: [Investigation scheduled/Monitoring]

### Predictions Validated (Last 30 Days)

✅ **[Prediction Name]** ([date])
- **Outcome**: [Caught pre-production/Prevented]
- **Value**: Saved ~[X]hr debugging
- **Accuracy**: Prediction confidence was [percentage]%

✅ **[Prediction Name]** ([date])
- [Same format]

**Prediction Accuracy**: [percentage]% ([N] correct / [N] total)
**Trend**: [Improving/Stable/Declining]

---
name: meta-health

## 📈 ROI Summary

### Investment

**Initial Setup**:
- Time spent: [X] hours
- Date started: [date]
- Age: [N] days

**Ongoing Maintenance**:
- Weekly: ~[X] hours
- Monthly: ~[X] hours
- Automation level: [percentage]% (↑ over time)

### Returns (Monthly Average)

**Time Savings**:
- Direct automation: [X] hours/month
- Improved velocity: [X] hours/month
- Prevented debugging: [X] hours/month
- **Total**: [X] hours/month

**Quality Improvements**:
- Bugs prevented: [N] ([~$X] value)
- Security issues caught: [N]
- Documentation drift prevented: [percentage]%

**Knowledge Captured**:
- Patterns documented: [N]
- Templates created: [N]
- Workflow optimizations: [N]

### ROI Calculation

**Monthly ROI**: [X] hours saved / [X] hours invested = **[X]x**

**Compound ROI** (Lifetime):

Total time invested: [X] hours Total time saved: [X] hours Bugs prevented value: ~$[X] Knowledge value: [N] reusable patterns

Compound Multiplier: [X]x (and growing)


**ROI Trend**:

Month 1: 0.5x (investment phase) Month 2: 1.8x (early returns) Month 3: 4.2x (compound effects) Month 6: 9.4x (current)


**Break-Even**: Achieved in Month [N]
**Payback Period**: [N] weeks

---
name: meta-health

## 📋 System Summary

### Quick Stats
- **Commands Executed**: [N] (last 30 days)
- **Most Used Command**: [command] ([percentage]%)
- **Most Effective Agent**: [agent] ([percentage]% success)
- **Patterns Detected**: [N]
- **Auto-Improvements**: [N] implemented
- **System Age**: [N] days

### Health Score: [N]/100

**Score Breakdown**:
- Velocity: [N]/20 ([description])
- Quality: [N]/20 ([description])
- Intelligence: [N]/20 ([description])
- ROI: [N]/20 ([description])
- Trend: [N]/20 ([description])

**Overall Status**: [🟢 Excellent | 🟡 Good | 🔴 Needs Improvement]

### Recommendations

**IMMEDIATE ACTION REQUIRED**:
[If any critical issues, list here]

**OPPORTUNITIES THIS WEEK**:
1. [Action item based on data]
2. [Action item based on data]

**LONG-TERM FOCUS**:
1. [Strategic recommendation]
2. [Strategic recommendation]

---
name: meta-health

## 📊 Appendix: Detailed Metrics

### Telemetry Summary
- Total executions: [N]
- Success rate: [percentage]%
- Average duration: [X] seconds
- Files changed: [N] total
- Tests added: [N] total

### Historical Data Points
- Suggestions generated: [N]
- Suggestions implemented: [N] ([percentage]%)
- Suggestions rejected: [N] ([percentage]%)
- Average ROI accuracy: [percentage]% (estimated vs actual)

### System Configuration
- Meta-learning version: [version]
- Telemetry started: [date]
- Plugins installed: [list]
- Update frequency: [frequency]

---
name: meta-health

**Dashboard Generated**: [timestamp]
**Next Update**: [scheduled time]
**Data Confidence**: [High/Medium/Low] (based on [N] data points)

**Actions**:
- Use `/meta-analyze` to deep dive into patterns
- Use `/meta-learn` to generate new improvement suggestions
- Use `/meta-implement` to deploy high-confidence improvements
- Use `/meta-predict` to see future risk predictions

Phase 5: Publish Dashboard (if --publish flag set)

If --publish is true, save dashboard to a public location:

if [ "$PUBLISH" = true ]; then
  echo ""
  echo "📊 Publishing dashboard..."

  # Create docs directory if it doesn't exist
  DOCS_DIR="$PLUGIN_ROOT/../../docs"
  mkdir -p "$DOCS_DIR"

  # Save dashboard
  DASHBOARD_FILE="$DOCS_DIR/system-health-$(date +%Y%m%d).md"
  # Dashboard content written by Write tool above

  # Also create/update latest symlink
  ln -sf "system-health-$(date +%Y%m%d).md" "$DOCS_DIR/system-health-latest.md"

  echo "✅ Dashboard published to: $DASHBOARD_FILE"
  echo "📄 Latest: $DOCS_DIR/system-health-latest.md"

  # If GitHub Pages configured, could push to gh-pages branch
  # git checkout gh-pages
  # cp $DASHBOARD_FILE index.md
  # git add index.md && git commit -m "Update health dashboard" && git push
fi

Phase 6: Send Email Summary (if --send-summary-email flag set)

If --send-email is true, generate and send email summary:

if [ "$SEND_EMAIL" = true ]; then
  echo ""
  echo "📧 Generating email summary..."

  # Create condensed email version
  EMAIL_SUBJECT="PSD Meta-Learning Health: [Status] - [Date]"
  EMAIL_BODY="
  System Health Summary - $(date +%Y-%m-%d)

  🚀 VELOCITY: [X]x (↑[percentage]% vs baseline)
  💰 ROI: [X]x compound multiplier
  ✅ QUALITY: [metrics summary]
  🧠 INTELLIGENCE: [agent performance summary]

  📊 THIS MONTH:
  • [X] hours saved
  • [N] auto-improvements implemented
  • [N] bugs prevented

  ⚠️  ALERTS:
  [List high-confidence predictions if any]

  📈 TRENDS:
  [Key positive trends]

  🎯 RECOMMENDED ACTIONS:
  [Top 3 action items]

  Full dashboard: [link]
  "

  # Send via mail command or API
  # echo "$EMAIL_BODY" | mail -s "$EMAIL_SUBJECT" hagelk@psd401.net

  echo "✅ Email summary prepared"
  echo "   (Email sending requires mail configuration)"
fi

Phase 7: Output Results

echo ""
echo "✅ Health dashboard generated!"
echo ""

if [ -n "$OUTPUT_FILE" ]; then
  echo "📝 Saved to: $OUTPUT_FILE"
fi

if [ "$PUBLISH" = true ]; then
  echo "📊 Published to docs/"
fi

if [ "$SEND_EMAIL" = true ]; then
  echo "📧 Email summary prepared"
fi

echo ""
echo "Next steps:"
echo "  • Review alerts and recommendations"
echo "  • Act on immediate action items"
echo "  • Track trends over time"
echo "  • Share dashboard with stakeholders"

Dashboard Generation Guidelines

Data Aggregation Best Practices

DO:

Calculate actual metrics from real data (don't estimate)
Show trends with visual indicators (▁▂▃▄▅▆▇█, ↑↓, 🟢🟡🔴)
Compare current vs baseline vs target
Include confidence levels for predictions
Provide actionable recommendations
Track ROI with concrete numbers

DON'T:

Show vanity metrics without context
Include data without trends
Make claims without evidence
Overwhelm with too many metrics
Ignore negative trends
Present data without interpretation

Handling Missing or Insufficient Data

If data is limited, clearly indicate:

## 📊 LIMITED DATA AVAILABLE

**Current Status**:
- System age: [N] days (minimum 30 days recommended for trends)
- Executions: [N] (minimum 50+ for statistics)
- Data completeness: [percentage]%

**Available Metrics** (limited confidence):
[Show what metrics can be calculated]

**Unavailable Metrics** (insufficient data):
- Agent evolution (needs 3+ generations)
- Trend analysis (needs 30+ days)
- ROI accuracy (needs completed suggestions)

**Recommendation**:
Continue using the system for [N] more days to enable full dashboard.

**Preliminary Health**: [Basic metrics available]

Trend Visualization

Use ASCII charts for quick visual trends:

Velocity over 12 weeks:
1.0x ████████
1.2x ██████████
1.5x ████████████
1.8x ██████████████
2.3x ██████████████████

ROI Compound Growth:
Month 1: ▁ 0.5x
Month 2: ▃ 1.8x
Month 3: ▅ 4.2x
Month 6: █ 9.4x

Health Score Calculation

Formula: Sum of weighted sub-scores

Velocity (20 points): Based on time_saved and productivity increase
- 1.0-1.5x = 10 pts
- 1.5-2.0x = 15 pts
- 2.0x+ = 20 pts
Quality (20 points): Based on test coverage, tech debt, security
- Each metric contributes 5-7 pts
Intelligence (20 points): Based on agent evolution and patterns learned
- Agent improvement avg >20% = 15+ pts
- Patterns documented >50 = 15+ pts
ROI (20 points): Based on compound multiplier
- 2-5x = 10 pts
- 5-10x = 15 pts
- 10x+ = 20 pts
Trend (20 points): Based on direction of key metrics
- All improving = 20 pts
- Mixed = 10-15 pts
- Declining = 0-10 pts

Total: 0-100 points

80-100: 🟢 Excellent
60-79: 🟡 Good
40-59: 🟡 Needs Improvement
<40: 🔴 Critical

Important Notes

Accuracy: All metrics must be based on actual data, never invented
Trends: Show direction and magnitude of change
Context: Always provide baseline and target for comparison
Actionable: Include specific recommendations based on data
Honest: Don't hide negative trends or problems
Visual: Use symbols and charts for quick scanning
Regular: Dashboard should be generated weekly or daily for trends

Example Usage Scenarios

Scenario 1: Daily Health Check

/meta-health

Quick health overview in terminal.

Scenario 2: Weekly Dashboard Publication

/meta-health --publish --output meta/health-$(date +%Y%m%d).md

Save dashboard and publish to docs.

Scenario 3: Monthly Stakeholder Report

/meta-health --publish --send-summary-email

Full dashboard with email summary to stakeholders.

name: meta-health

Remember: The health dashboard demonstrates compound engineering value. Show concrete ROI, track trends over time, and provide actionable insights that drive continuous improvement.