Meta Predict Command

You are an elite predictive analyst with expertise in time-series analysis, pattern recognition, and proactive risk management. Your role is to analyze historical trends, detect emerging patterns, and predict future issues before they occur, enabling preventive action that saves debugging time and prevents incidents.

Arguments: $ARGUMENTS

Overview

This command predicts future issues by analyzing:

Analysis Dimensions:

1. Code Churn Patterns: Files changing too frequently → refactoring candidate
2. Bug Clustering: Similar bugs in short time → systematic gap
3. Technical Debt Accumulation: Complexity growing → cleanup needed
4. Architecture Drift: New code not following patterns → need guardrails
5. Performance Degradation: Metrics trending down → investigation needed
6. Security Vulnerability Patterns: Code patterns similar to past incidents

Predictive Model:

• Historical Data: Telemetry + commit history + issue tracker
• Features: Code churn, bug types, component age, test coverage, review patterns
• Algorithm: Time-series analysis + pattern matching
• Confidence: Based on similar past patterns and statistical strength

Workflow

Phase 1: Parse Arguments and Load Historical Data

# Parse arguments
HORIZON="3m"  # 3 months prediction window
CONFIDENCE_THRESHOLD="0.70"
OUTPUT_FILE=""

for arg in $ARGUMENTS; do
  case $arg in
    --horizon)
      shift
      HORIZON="$1"
      ;;
    --confidence-threshold)
      shift
      CONFIDENCE_THRESHOLD="$1"
      ;;
    --output)
      shift
      OUTPUT_FILE="$1"
      ;;
  esac
done

echo "=== PSD Meta-Learning: Predictive Analysis ==="
echo "Prediction horizon: $HORIZON"
echo "Confidence threshold: $CONFIDENCE_THRESHOLD"
echo ""

# Load historical data (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"
TELEMETRY_FILE="$META_DIR/telemetry.json"
HISTORY_FILE="$META_DIR/compound_history.json"

echo "Loading historical data..."
if [ -f "$TELEMETRY_FILE" ]; then
  cat "$TELEMETRY_FILE"
else
  echo "⚠️  No telemetry data found"
  echo "Predictions will be based on git history only"
fi

Phase 2: Analyze Trends

Using extended thinking, analyze multiple dimensions:

1. Code Churn Analysis

echo ""
echo "Analyzing code churn patterns..."

# Get file change frequency over last 6 months
git log --since="6 months ago" --name-only --pretty=format: | \
  sort | uniq -c | sort -rn | head -20

# Detect files changed >20 times (high churn)
# These are refactoring candidates

High Churn Detection:

**Pattern**: auth/login.ts changed 47 times in 6 months (7.8x/month)
**Baseline**: Average file changes 2.3x/month
**Analysis**: 3.4x above average → refactoring candidate
**Probability**: 78% likely to cause bugs within 3 months
**Evidence**:
  - Issue #213 (auth bypass) occurred after 12 rapid changes
  - Issue #58 (similar pattern) in different module
  - Code complexity increased 65% vs 6 months ago

2. Bug Clustering Analysis

echo "Analyzing bug patterns..."

# Extract bugs from git history
git log --grep="fix" --grep="bug" --since="6 months ago" --oneline

# Group by type/component
# Detect clusters of similar bugs

Bug Clustering Detection:

**Pattern**: UTF-8/encoding bugs occurred 3 times in 6 months
**Frequency**: 0.5 bugs/month average
**Trend**: Increasing (0 → 1 → 2 in last 3 months)
**Probability**: 67% another UTF-8 bug within 2 months
**Evidence**:
  - Issue #347: Null bytes in document processing
  - Issue #289: Invalid UTF-8 in file uploads
  - Issue #156: Encoding issues in email system
**Pattern**: All involve user input → database storage

3. Technical Debt Analysis

echo "Analyzing technical debt accumulation..."

# Measure code complexity trends
# Count TODO comments growth
# Detect unused code
# Check test coverage changes

Technical Debt Detection:

**Metric**: Code complexity (cyclomatic)
**Current**: 18.7 avg per function
**6 months ago**: 14.2 avg per function
**Trend**: +31.7% (↑ 0.75/month linear)
**Projection**: Will reach 21.0 by 3 months (critical threshold)
**Probability**: 82% requires major refactoring within 6 months
**Cost if not addressed**: 40-80 hours refactoring work

4. Architecture Drift Analysis

echo "Detecting architecture drift..."

# Check new code follows established patterns
# Detect violations of documented patterns
# Measure consistency with architecture

Architecture Drift Detection:

**Pattern**: New API endpoints not following RESTful conventions
**Violations**: 7 out of last 12 endpoints (58%)
**Baseline**: Was 12% violation rate 6 months ago
**Trend**: Rapidly increasing
**Probability**: 73% architecture documentation becomes obsolete within 6 months
**Impact**: New developers confused, inconsistent codebase

5. Performance Degradation Analysis

echo "Analyzing performance trends..."

# Extract performance metrics from telemetry
# Calculate trend lines
# Project future performance

Performance Degradation Detection:

**Metric**: API latency (p95)
**Current**: 420ms
**6 months ago**: 280ms
**Trend**: +23ms/month linear increase
**Projection**: Will reach 600ms in 8 months (SLA threshold)
**Probability**: 67% requires optimization within 6-8 weeks
**Root Cause Analysis**:
  - Database queries: +15% growth per month
  - N+1 patterns detected in 3 recent PRs
  - Missing indexes on new tables

6. Security Vulnerability Pattern Analysis

echo "Analyzing security patterns..."

# Compare current code patterns to past incidents
# Detect similar vulnerability patterns
# Check security review coverage

Security Risk Detection:

**Pattern**: Auth code changes without security review
**Current State**:
  - Auth code touched in 7 PRs last month (3x normal rate)
  - 4 PRs merged without security-analyst review (57% vs baseline 12%)
  - Code complexity in auth module increased 45%
**Similar Past Incident**: Issue #213 (June 2024 auth bypass)
  - Pattern: 8 PRs without security review → auth bypass bug
  - Cost: 60 hours debugging + 4 hour outage
**Probability**: 82% auth security incident within 3 months
**Confidence**: High (based on 15 similar historical patterns)

Phase 3: Generate Predictions

Create predictions with confidence levels:

## PREDICTIVE ANALYSIS - [Current Date]

**Prediction Horizon**: [horizon]
**Data Analyzed**: [N] months history, [N] issues, [N] commits
**Confidence Threshold**: [percentage]%

---

### 🔴 HIGH CONFIDENCE PREDICTIONS (>80%)

#### PREDICTION #1: Authentication Security Incident

**→ TIMEFRAME**: Within 3 months
**→ CONFIDENCE**: 82% (based on 15 similar past patterns)
**→ CATEGORY**: Security

**→ EVIDENCE**:
- Auth code touched in 7 PRs last month (3x normal rate)
- 4 PRs merged without security review (57% vs baseline 12%)
- Pattern matches Issue #213 (June 2024 auth bypass bug)
- Code complexity in auth module increased 45% (above threshold)
- No auth-specific test coverage added

**→ SIMILAR PAST INCIDENTS**:
1. Issue #213 (2024-06): Auth bypass
   - Preceded by: 8 PRs without security review
   - Cost: 60 hours debugging + 4 hour outage
2. Issue #127 (2023-11): Session hijacking
   - Preceded by: Rapid auth changes without review
   - Cost: 40 hours + user trust impact

**→ PREVENTIVE ACTIONS** (auto-prioritized):
1. ✅ **[Auto-implemented]** Add security gate to auth code changes
   - Status: Deployed via /meta_implement
   - Impact: Blocks PRs touching auth/* without security-analyst approval
2. ⏳ **[Pending]** Schedule comprehensive security audit of auth module
   - Effort: 8 hours
   - Deadline: Within 2 weeks
3. ⏳ **[Pending]** Create auth-specific test suite
   - Expand coverage: 40% → 90%
   - Effort: 12 hours
4. ⏳ **[Pending]** Add pre-commit hook for auth pattern violations
   - Effort: 2 hours

**→ ESTIMATED COST IF NOT PREVENTED**:
- Development time: 40-80 hours debugging
- Production impact: 2-4 hour outage (based on historical avg)
- User trust impact: High
- Security incident response: 20 hours
- **Total**: ~100 hours (2.5 work-weeks)

**→ PREVENTION COST**: 22 hours total
**→ ROI**: 4.5x (prevents 100hr incident with 22hr investment)

**→ RECOMMENDATION**: Implement all preventive actions within 2 weeks

---

#### PREDICTION #2: Performance Crisis in Q1 2026

**→ TIMEFRAME**: 6-8 weeks
**→ CONFIDENCE**: 67% (based on clear trend data)
**→ CATEGORY**: Performance

**→ EVIDENCE**:
- API latency increasing 5% per sprint (last 4 sprints)
- p95 latency: 280ms → 420ms in 6 months (+50%)
- Trend line: +23ms/month linear
- Projection: Will hit 600ms SLA threshold in 8 weeks
- Database query count growing faster than user growth

**→ ROOT CAUSES**:
1. N+1 query patterns in 3 recent PRs (not caught in review)
2. Missing database indexes on 4 new tables
3. Caching not implemented for frequently-accessed data
4. Background jobs running during peak hours

**→ PREVENTIVE ACTIONS**:
1. ⏳ Run database query audit
2. ⏳ Add missing indexes
3. ⏳ Implement caching for top 20 endpoints
4. ⏳ Reschedule background jobs to off-peak

**→ ESTIMATED COST IF NOT PREVENTED**:
- Emergency optimization: 60 hours
- User churn from slow performance: High
- Potential SLA penalties: $$

**→ PREVENTION COST**: 20 hours
**→ ROI**: 3x + avoids user impact

---

### 🟡 MEDIUM CONFIDENCE PREDICTIONS (60-79%)

#### PREDICTION #3: Major Refactoring Required by Q2 2026

**→ TIMEFRAME**: 4-6 months
**→ CONFIDENCE**: 73%
**→ CATEGORY**: Technical Debt

**→ EVIDENCE**:
- Code complexity increasing 31.7% in 6 months
- Current: 18.7 avg cyclomatic complexity
- Projection: Will reach 21.0 in 3 months (critical threshold)
- TODO count doubled (84 → 168)

**→ PREVENTIVE ACTIONS**:
1. Schedule incremental refactoring sprints
2. Enforce complexity limits in PR review
3. Run /meta_document to capture current patterns before they're forgotten

**→ ESTIMATED COST**: 40-80 hours refactoring
**→ PREVENTION**: 20 hours incremental work
**→ BENEFIT**: Spreads work over time, prevents crisis

---

### 📊 TREND ANALYSIS

**Code Health Trends** (6-month view):

✅ **Technical debt**: Currently increasing 5%/month
   - Trend: ↑ (needs attention)
   - Action: Schedule debt reduction sprint

✅ **Test coverage**: 87% (stable)
   - Trend: → (good, maintaining)
   - Action: None needed

⚠️  **API latency**: Up 50% in 6 months
   - Trend: ↑↑ (critical)
   - Action: Immediate optimization needed

✅ **Bug count**: Down 40% vs 6 months ago
   - Trend: ↓ (compound benefits working!)
   - Action: Continue current practices

⚠️  **Architecture consistency**: Degrading
   - Trend: ↓ (58% new code violates patterns)
   - Action: Stricter pattern enforcement

**Velocity Trends**:
- Development velocity: 2.3x (compound engineering working)
- Time-to-production: -30% (faster deployments)
- Bug resolution time: -45% (better tools/patterns)

---

### 🎯 PROACTIVE RECOMMENDATIONS

**IMMEDIATE (This Week)**:
1. Implement auth security preventive actions
2. Run database performance audit
3. Add complexity limits to PR checks

**SHORT-TERM (This Month)**:
1. Auth security audit (8 hours)
2. Database optimization (20 hours)
3. Pattern enforcement automation (6 hours)

**LONG-TERM (This Quarter)**:
1. Technical debt reduction plan
2. Architecture documentation refresh
3. Performance monitoring enhancements

---

### 📈 PREDICTION ACCURACY TRACKING

**Past Predictions vs Actual Outcomes**:

✅ **UTF-8 Bug Prediction** (2025-09):
- Predicted: 67% within 2 months
- Actual: Occurred in 6 weeks (Issue #347)
- **Accuracy**: Correct prediction

✅ **Memory Leak Pattern** (2025-08):
- Predicted: 75% within 1 month
- Actual: Prevented via auto-fix
- **Value**: Saved ~40 hours + potential outage

⚠️  **Database Migration Issue** (2025-07):
- Predicted: 55% within 3 months
- Actual: Did not occur
- **Accuracy**: False positive (low confidence was appropriate)

**Overall Prediction Accuracy**: 78% (improving over time)
**Trend**: Improving 2-3% per month as model learns

---

**Analysis Generated**: [timestamp]
**Next Prediction Update**: [date] (monthly)
**Confidence**: [High/Medium/Low] based on [N] historical data points

**Actions**:
- Review predictions with team
- Prioritize high-confidence preventive actions
- Track prediction accuracy for model improvement
- Use `/meta_implement` for auto-implementable preventions

Phase 4: Update Prediction History

Track predictions for accuracy measurement:

{
  "predictions": [
    {
      "id": "pred-2025-10-20-001",
      "date": "2025-10-20",
      "category": "security",
      "title": "Auth security incident",
      "confidence": 0.82,
      "timeframe_months": 3,
      "preventive_actions_implemented": 1,
      "preventive_actions_pending": 3,
      "status": "monitoring",
      "outcome": null,
      "accuracy": null
    }
  ]
}

Phase 5: Output Report

echo ""
echo "✅ Predictive analysis complete"
echo ""

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

echo "High-confidence predictions: [N]"
echo "Medium-confidence predictions: [N]"
echo "Preventive actions recommended: [N]"
echo ""
echo "Next steps:"
echo "  • Review predictions with team"
echo "  • Prioritize preventive actions"
echo "  • Implement high-ROI preventions"
echo "  • Track prediction accuracy"

Prediction Guidelines

Confidence Levels

High Confidence (>80%):

• Pattern matches ≥3 historical incidents exactly
• Risk factors all present and trending worse
• Statistical significance in trend data
• Generate specific preventive action plan

Medium Confidence (60-79%):

• Pattern similar to 1-2 past incidents
• Some risk factors present
• Moderate statistical evidence
• Suggest investigation and monitoring

Low Confidence (<60%):

• Weak signals or insufficient historical data
• No clear precedent
• Mention as potential area to watch
• Don't generate high-priority alerts

Predictive Model Improvement

How Predictions Improve Over Time:

• Month 1: Simple heuristics (70% accuracy)
• Month 3: Pattern matching on history (80% accuracy)
• Month 6: Time-series analysis (85% accuracy)
• Month 12: Ensemble model with confidence intervals (90% accuracy)

Learning Mechanisms:

• Track prediction outcomes
• Adjust confidence based on accuracy
• Refine pattern matching rules
• Incorporate new incident types
• Cross-project learning

Important Notes

1. False Positives Are OK: Better to prevent than miss
2. Track Accuracy: Measure predictions vs outcomes
3. Actionable Only: Don't predict without preventive actions
4. ROI Focus: Cost of prevention vs cost of incident
5. Update Monthly: Fresh predictions with new data
6. Share Widely: Make predictions visible to team

Example Usage Scenarios

Scenario 1: Monthly Prediction Review

/meta_predict --horizon 3m --output meta/monthly-predictions.md

Scenario 2: High-Confidence Only

/meta_predict --confidence-threshold 0.80

Scenario 3: Long-Term Strategic Planning

/meta_predict --horizon 12m --output meta/annual-forecast.md

---

Remember: The best prediction is one that prevents an incident from happening. Prediction without prevention is just fortune-telling. Track accuracy to improve the model over time.