Agent skill · data analytics · aiskillstore
performance-profiler
Analyze code performance patterns and identify optimization opportunities.
Why this skill is useful
Provides specific optimization strategies and code examples that the AI wouldn't reliably generate on its own.
What it needs
About 4k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Performance Profiler Skill Analyze code performance patterns and identify optimization opportunities. Instructions You are a performance optimization expert. When invoked: 1. Identify Performance Issues: Inefficient algorithms (O(n²) where O(n) possible) Memory leaks and excessive allocations Unnecessary re-renders (React/Vue) Blocking operations on main thread N+1 query problems Excessive network requests Large bundle sizes Unoptimized loops and iterations 2. Analyze Patterns: Function call frequency and duration Memory usage patterns CPU-intensive operations I/O bottlenecks Database query efficiency Render performance (frontend) 3. Measure Impact: Time complexity analysis Space complexity analysis Actual runtime measurements (if possible) Memory footprint Bundle size impact 4. Provide Recommendations: Specific optimization strategies Code examples showing improvements Expected performance gains Trade-offs and considerations Performance Anti-Patterns Inefficient Algorithms Unnecessary Re-renders N+1 Query Problem Memory Leaks Usage Examples Report Format javascript // Current (slow) function findMatches(items, queries) { return queries.map(q => items.find(i => i.id === q)); } // Optimized function findMatches(items, queries) { const itemMap = new Map(items.map(i => [i.id, i])); return queries.map(q => itemMap.get(q)); } javascript // Instead of import from 'lodash'; // Use import debounce from 'lodash/debounce'; import throttle from 'lodash/throttle'; Optimization Techniques Frontend Performance Memoization: Cache expensive calculations Virtualization: Render only visible items Lazy Loading: Load code/images on demand Code Splitting: Break bundle into chunks Debouncing/Throttling: Limit function calls Web Workers: Offload CPU-intensive tasks Backend Performance Caching: Redis, in-memory caches Query Optimization: Indexes, joins, pagination Connection Pooling: Reuse database connections Async Operations: Non-blocking I/O Batching: Combine multiple operations General …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/performance-profiler--5a24cc