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054c0e56e9
Removed the deprecated 'model: sonnet' field from command frontmatter to fix compatibility issues with Claude Code. Commands will now inherit the model from the active conversation instead of using a hardcoded deprecated model identifier. Changes: - Removed 'model: sonnet' from 142 command files across all categories - Commands now use default model inheritance behavior - Preserved 'model: claude-sonnet-4-5-20250929' in gemini-review.md (valid model) - 67 commands without model field remain unchanged This resolves the "model: sonnet not found" error reported in GitHub issues and aligns with Claude Code's recommended practice of letting commands inherit the conversation model. Affected categories: - Database/Supabase commands (8 files) - Documentation commands (8 files) - Deployment commands (11 files) - Setup commands (14 files) - Performance commands (8 files) - Team commands (14 files) - Security commands (6 files) - NextJS/Vercel commands (10 files) - Sync commands (14 files) - And more across other categories 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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allowed-tools: Read, Bash, Grep, Glob
argument-hint: [target-area] | --frontend | --backend | --full
description: Comprehensive performance audit with metrics, bottleneck identification, and optimization recommendations
Performance Audit
Conduct comprehensive performance audit: $ARGUMENTS
Current Performance Context
- Bundle analysis: !
npm run build -- --analyze 2>/dev/null || echo "No build analyzer" - Dependencies: !
npm list --depth=0 --prod 2>/dev/null | head -10 - Build time: !
time npm run build >/dev/null 2>&1 || echo "No build script" - Performance config: @webpack.config.js or @vite.config.js or @next.config.js (if exists)
Task
Conduct comprehensive performance audit following these steps:
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
Performance Monitoring
- Check existing performance metrics and monitoring
- Identify key performance indicators (KPIs) to track
- Review alerting and performance thresholds
- Suggest performance testing strategies
-
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
-
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.