Performance Benchmarking

Triggers

• performance benchmark
• load testing
• stress testing
• Core Web Vitals
• page speed
• response time
• throughput testing
• scalability test
• performance optimization
• capacity planning
• LCP optimization
• performance budget
• endurance testing
• performance SLA
• bottleneck analysis

Instructions

Performance Baseline and Requirements

• Establish current performance baselines across all system components using shell_execute
• Define performance requirements and SLA targets with stakeholder alignment
• Identify critical user journeys and high-impact performance scenarios
• Set up performance monitoring infrastructure and data collection
• Use browser_navigate for Core Web Vitals measurement

Comprehensive Testing Strategy

• Design test scenarios covering load, stress, spike, and endurance testing
• Create realistic test data and user behavior simulation
• Plan test environment setup that mirrors production characteristics
• Implement statistical analysis methodology for reliable results

Performance Analysis and Optimization

• Execute comprehensive performance testing with detailed metrics collection
• Identify bottlenecks through systematic analysis of results
• Provide optimization recommendations with cost-benefit analysis
• Validate optimization effectiveness with before/after comparisons
• Use knowledge_write to store performance baselines and optimization patterns

Core Web Vitals Optimization

• Optimize for Largest Contentful Paint (LCP < 2.5s)
• Optimize for First Input Delay (FID < 100ms)
• Optimize for Cumulative Layout Shift (CLS < 0.1)
• Implement code splitting, lazy loading, and CDN optimization
• Monitor Real User Monitoring (RUM) data alongside synthetic metrics
• Use web_search for performance optimization techniques and benchmarks

Methodology Standards

• Always establish baseline performance before optimization attempts
• Use statistical analysis with confidence intervals for measurements
• Test under realistic load conditions simulating actual user behavior
• Consider performance impact of every optimization recommendation
• Prioritize user-perceived performance over technical metrics alone
• Test across different network conditions and device capabilities

Deliverables

Performance Analysis Report Template

# [System Name] Performance Analysis Report

## Performance Test Results
**Load Testing**: [Normal load performance with detailed metrics]
**Stress Testing**: [Breaking point analysis and recovery behavior]
**Scalability Testing**: [Performance under increasing load scenarios]
**Endurance Testing**: [Long-term stability and memory leak analysis]

## Core Web Vitals Analysis
**Largest Contentful Paint**: [LCP measurement with optimization recommendations]
**First Input Delay**: [FID analysis with interactivity improvements]
**Cumulative Layout Shift**: [CLS measurement with stability enhancements]
**Speed Index**: [Visual loading progress optimization]

## Bottleneck Analysis
**Database Performance**: [Query optimization and connection pooling analysis]
**Application Layer**: [Code hotspots and resource utilization]
**Infrastructure**: [Server, network, and CDN performance analysis]
**Third-Party Services**: [External dependency impact assessment]

## Performance ROI Analysis
**Optimization Costs**: [Implementation effort and resource requirements]
**Performance Gains**: [Quantified improvements in key metrics]
**Business Impact**: [User experience improvement and conversion impact]
**Cost Savings**: [Infrastructure optimization and efficiency gains]

## Optimization Recommendations
**High-Priority**: [Critical optimizations with immediate impact]
**Medium-Priority**: [Significant improvements with moderate effort]
**Long-Term**: [Strategic optimizations for future scalability]

---
**Performance Status**: [MEETS/FAILS SLA requirements]
**Scalability Assessment**: [Ready/Needs Work for projected growth]

k6 Load Test Configuration

export const options = {
  stages: [
    { duration: '2m', target: 10 },   // Warm up
    { duration: '5m', target: 50 },   // Normal load
    { duration: '2m', target: 100 },  // Peak load
    { duration: '5m', target: 100 },  // Sustained peak
    { duration: '2m', target: 200 },  // Stress test
    { duration: '3m', target: 0 },    // Cool down
  ],
  thresholds: {
    http_req_duration: ['p(95)<500'],
    http_req_failed: ['rate<0.01'],
  },
};

Success Metrics

• 95% of systems consistently meet or exceed performance SLA requirements
• Core Web Vitals scores achieve "Good" rating for 90th percentile users
• Performance optimization delivers 25% improvement in key user experience metrics
• System scalability supports 10x current load without significant degradation
• Performance monitoring prevents 90% of performance-related incidents

Verify

• The test suite was actually executed and exit code/output is captured in the transcript, not just authored
• Pass/fail counts are reported as numbers (e.g., '42 passed, 0 failed'), not 'all tests pass'
• New tests cover at least one negative/edge case in addition to the happy path; the cases are listed
• Coverage delta or affected modules are reported when the project tracks coverage; a baseline number is cited
• For flaky or timing-sensitive tests, the run was repeated at least 3 times and pass-rate is reported
• Any skipped or xfail tests introduced are listed with a reason and an issue/TODO link