[ACTIVATE:E2E_VALIDATOR_V1]

# Before skill activation:
./lib/worktree-manager.sh create 5 1
cd ./worktrees/phase-5-task-1

# Validate isolation:
./hooks/worktree-enforcer.sh enforce

# E2E validation with isolation

Stage 1: E2E Workflow Analysis
         - Extract user journeys from PRD
         - Analyze existing E2E tests
         - Calculate coverage gaps
Stage 2: Create Missing E2E Tests
         - Happy paths
         - Error scenarios
         - Cross-browser
         - Mobile viewports
Stage 3: Production Readiness Scoring
         - Testing (30%)
         - Security (25%)
         - Ops (20%)
         - Docs (15%)
         - Stakeholders (10%)
Stage 4: Go/No-Go Decision
         - Score ≥90% → GO
         - Score <90% → NO-GO + remediation plan
Stage 5: Generate Report & Signal

tests/visual/
├── viz_[feature]_accuracy.py    # Numerical accuracy validation
├── viz_[feature]_performance.py # Performance target validation
├── viz_[feature]_coverage.py    # Spatial coverage validation
└── output/
    ├── [feature]_accuracy.png
    └── visual-test-results.json

#!/usr/bin/env python3
"""
Visual validation for FR-XXX-X.X
Generated from PRD Section 8.3
"""
import matplotlib.pyplot as plt
import numpy as np
from pathlib import Path

def visualize_[feature]():
    """
    Validates [requirement description]
    Returns: bool - True if validation passes
    """
    # 1. Generate test data
    test_inputs = np.linspace(0, 10, 100)
    expected_outputs = [...]  # From requirement spec
    actual_outputs = [...]    # From component under test

    # 2. Compute errors
    errors = np.abs(actual_outputs - expected_outputs)
    max_error = np.max(errors)
    threshold = 0.1  # From PRD requirement

    # 3. Create visualization
    fig, axes = plt.subplots(1, 2, figsize=(14, 6))

    # Plot 1: Results comparison
    axes[0].plot(test_inputs, expected_outputs, 'b-', label='Expected')
    axes[0].plot(test_inputs, actual_outputs, 'r--', label='Actual')
    axes[0].legend()
    axes[0].set_title('[Feature]: Results Comparison')

    # Plot 2: Error distribution with threshold
    axes[1].hist(errors, bins=50, alpha=0.7)
    axes[1].axvline(x=threshold, color='r', linestyle='--',
                    label=f'Requirement: {threshold}')
    axes[1].legend()
    axes[1].set_title('Error Distribution vs Requirement')

    # 4. Save output
    output_dir = Path(__file__).parent / "output"
    output_dir.mkdir(exist_ok=True)
    plt.savefig(output_dir / "[feature]_validation.png", dpi=150)

    # 5. Determine pass/fail
    passed = np.all(errors < threshold)

    if passed:
        print(f"✅ PASS: All errors below threshold ({threshold})")
    else:
        print(f"❌ FAIL: Max error {max_error:.6f} exceeds threshold")

    return passed

if __name__ == "__main__":
    import sys
    passed = visualize_[feature]()
    sys.exit(0 if passed else 1)

{
  "timestamp": "ISO8601",
  "tests_run": 3,
  "tests_passed": 2,
  "tests_failed": 1,
  "results": [
    {
      "test": "viz_accuracy.py",
      "passed": true,
      "output_image": "accuracy_validation.png"
    }
  ]
}

{
  "phase": 10,
  "status": "success",
  "summary": {
    "e2e_workflows": N,
    "e2e_coverage": 100,
    "production_score": 92,
    "decision": "GO"
  },
  "next_phase": 11,
  "trigger_next": true
}

tests/e2e/
├── [workflow].e2e.test.js
└── ...

.taskmaster/
├── PHASE10_COMPLETION_REPORT.md
└── .signals/phase10-complete.json