What problem does it solve?
Code often ships with hidden performance problems like N+1 database queries, unpaginated large datasets, unoptimized images, and O(n²) loops that only surface under production load. This Skill gives the AI a concrete performance checklist so these issues are caught and prevented during code generation rather than after deployment.
Core Features & Use Cases
- Query Optimization: Bans N+1 queries via JOINs, batch loading, and dataloaders; requires pagination, proper indexes, and selecting only needed columns.
- Caching Strategy: Guides cache usage for read-heavy data with TTLs, write-invalidation, and protection against cache penetration and avalanche.
- Frontend Performance: Enforces code splitting, lazy loading, WebP/AVIF images with dimension declarations, critical CSS inlining, and virtual scrolling for long lists.
- Algorithm & Concurrency Rules: Prevents nested loops on large datasets, prefers Map/Set lookups, batch operations, and debounce/throttle for high-frequency events.
- Use Case: When asking the AI to build a product listing API with images, it will automatically paginate results, avoid N+1 queries, and optimize image delivery.
Quick Start
Ask the AI to review or write performance-sensitive code such as a database-backed list endpoint, and it will apply these optimization rules and run the delivery checklist.