Files
Dani 054c0e56e9 fix(commands): Remove deprecated 'model: sonnet' field from 142 command files
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>
2025-10-21 10:08:58 -04:00

3.5 KiB

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:

  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.