Performance Audit Command
Audit application performance metrics
Instructions
Conduct a comprehensive performance audit following these steps:
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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
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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
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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
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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
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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
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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
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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
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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
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Performance Monitoring
- Check existing performance metrics and monitoring
- Identify key performance indicators (KPIs) to track
- Review alerting and performance thresholds
- Suggest performance testing strategies
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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
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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.