/performance-audit

Performance Audit Command

Audit application performance metrics

Instructions

Conduct a comprehensive performance audit following these steps:

1. **Technology Stack Analysis**

• Identify the primary language, framework, and runtime environment
• Review build tools and optimization configurations
• Check for performance monitoring tools already in place

2. **Code Performance Analysis**

• Identify inefficient algorithms and data structures
• Look for nested loops and O(n²) operations
• Check for unnecessary computations and redundant operations
• Review memory allocation patterns and potential leaks

3. **Database Performance**

• Analyze database queries for efficiency
• Check for missing indexes and slow queries
• Review connection pooling and database configuration
• Identify N+1 query problems and excessive database calls

4. **Frontend Performance (if applicable)**

• Analyze bundle size and chunk optimization
• Check for unused code and dependencies
• Review image optimization and lazy loading
• Examine render performance and re-render cycles
• Check for memory leaks in UI components

5. **Network Performance**

• Review API call patterns and caching strategies
• Check for unnecessary network requests
• Analyze payload sizes and compression
• Examine CDN usage and static asset optimization

6. **Asynchronous Operations**

• Review async/await usage and promise handling
• Check for blocking operations and race conditions
• Analyze task queuing and background processing
• Identify opportunities for parallel execution

7. **Memory Usage**

• Check for memory leaks and excessive memory consumption
• Review garbage collection patterns
• Analyze object lifecycle and cleanup
• Identify large objects and unnecessary data retention

8. **Build & Deployment Performance**

• Analyze build times and optimization opportunities
• Review dependency bundling and tree shaking
• Check for development vs production optimizations
• Examine deployment pipeline efficiency

9. **Performance Monitoring**

• Check existing performance metrics and monitoring
• Identify key performance indicators (KPIs) to track
• Review alerting and performance thresholds
• Suggest performance testing strategies

10. **Benchmarking & Profiling**

• Run performance profiling tools appropriate for the stack
• Create benchmarks for critical code paths
• Measure before and after optimization impact
• Document performance baselines

11. **Optimization Recommendations**

• Prioritize optimizations by impact and effort
• Provide specific code examples and alternatives
• Suggest architectural improvements for scalability
• Recommend appropriate performance tools and libraries

Include specific file paths, line numbers, and measurable metrics where possible. Focus on high-impact, low-effort optimizations first.