Does this skill work for both frontend and backend code?

Yes, it includes specialized optimization strategies for web performance (bundle size, re-renders) and backend systems (database queries, parallel processing).

Can it help me identify the cause of slow API responses?

Absolutely. It provides guidance on using profilers and load-testing tools like 'wrk' and 'ab' to find exact bottlenecks in your request-response cycle.

What is the 'measure first' approach recommended by this skill?

It emphasizes establishing a performance baseline using profiling tools before making changes, ensuring that optimizations target actual bottlenecks rather than perceived ones.

Does it support specific database types?

While the principles are universal, it provides specific examples for SQL optimization, including index management, join efficiency, and pagination techniques.

Performance Optimizer

Name: Performance Optimizer
Author: a-ariff

bya-ariff

Analíticas y Monitorización

Identifies and resolves performance bottlenecks across frontend, backend, and database layers using a data-driven measurement philosophy.

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The Performance Optimizer skill transforms Claude into a specialized agent for speed and efficiency, following the core principle of 'measure first, optimize second.' It provides comprehensive patterns for profiling code, identifying slow database queries, reducing algorithmic complexity, and managing memory leaks. Whether you are dealing with bloated JavaScript bundles, N+1 query problems, or inefficient network requests, this skill helps you implement industry-standard optimizations like caching, lazy loading, and virtualization while maintaining code readability and measurable impact.

Características Principales

Database optimization patterns for N+1 queries, indexing, and field selection
Algorithmic complexity refactoring from O(n²) to O(n) or O(1)
Frontend performance strategies including memoization, code splitting, and virtualization
Memory management techniques to identify leaks and optimize object retention
0 GitHub stars
Systematic profiling workflows for Node.js, Python, and Web environments

Casos de Uso

Refactoring backend services and database queries to handle high-concurrency traffic
Optimizing slow-loading web applications to improve Core Web Vitals and user experience
Reducing infrastructure costs by improving CPU and memory efficiency in cloud environments

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Características Principales

Database optimization patterns for N+1 queries, indexing, and field selection
Algorithmic complexity refactoring from O(n²) to O(n) or O(1)
Frontend performance strategies including memoization, code splitting, and virtualization
Memory management techniques to identify leaks and optimize object retention
0 GitHub stars
Systematic profiling workflows for Node.js, Python, and Web environments

Casos de Uso

Refactoring backend services and database queries to handle high-concurrency traffic
Optimizing slow-loading web applications to improve Core Web Vitals and user experience
Reducing infrastructure costs by improving CPU and memory efficiency in cloud environments