/performance-audit
Audit application performance metrics
$ npx -y skills add qdhenry/Claude-Command-Suite --agent claude-codeHow it fires
How this command gets triggered: by you, by Claude, or both.
- Fires itselfClaude auto-loads it when your prompt matches the work.
- You can call itInvoke it directly when you want it.
- Slash command
/performance-audit
Context preview
What this command does when you run it.
Audit application performance metrics
Command definition
performance-audit.mdPerformance 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.
Read more
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.
A comprehensive development toolkit designed following Anthropic's Claude Code Best Practices for AI-assisted software development.
Repo: qdhenry/Claude-Command-Suite
Other commands on claude-command-suite.
- /boundary-bbcr-fallback
Execute automatic BBCR (Collapse-Rebirth Correction) when knowledge boundaries are exceeded or reasoning fails.
Open command - /boundary-detect
Analyze semantic position relative to knowledge boundaries to prevent hallucination and identify uncertainty zones.
Open command - /boundary-heatmap
Generate a visual heatmap of knowledge boundaries showing safe zones, risk areas, and semantic coverage.
Open command - /boundary-risk-assess
Evaluate the current risk level and provide detailed analysis of potential hallucination or reasoning failure.
Open command - /boundary-safe-bridge
Find and construct semantic bridges to safely navigate from current position to target concept without crossing dangerous boundaries.
Open command - /optimize-prompt
Takes an input prompt and returns ONLY a token-optimized version that preserves meaning while minimizing token count. Based on LLM tokenization principles: common words tokenize more efficiently, unusual words break into more tokens, and conciseness reduces cost.
Open command