Can I use this for both frontend and backend tasks?

Yes, it covers frontend optimizations like memoization and image lazy loading, as well as backend strategies like multi-layer caching and async processing.

What performance metrics does this skill target?

It focuses on industry-standard metrics including Core Web Vitals (LCP, FID, CLS) for frontend applications and P50/P95 latency targets for API services.

How does this skill optimize database performance?

It identifies common issues like N+1 query problems and lack of pagination, suggesting JOINs and efficient data fetching patterns to reduce database latency.

Does it provide automated profiling?

It provides standardized commands for Node.js, Python, and Lighthouse profiling to help you establish performance baselines before and after applying changes.

Application Performance Optimizer

Name: Application Performance Optimizer
Author: Ven0m0

byVen0m0

Analíticas y Monitorización

Analyzes and optimizes full-stack application performance across frontend, backend, and database layers to reduce latency and improve user experience.

Acerca de

The Application Performance Optimizer is a comprehensive skill designed to diagnose and resolve bottlenecks across the entire software stack. It provides a structured workflow for measuring baselines, identifying performance drains like N+1 queries or heavy bundle sizes, and implementing industry-standard fixes such as multi-layer caching, code splitting, and algorithmic improvements. Whether you are dealing with slow page loads, high CPU usage, or database contention, this skill guides Claude to deliver data-driven optimizations that meet Core Web Vitals and API latency targets.

Características Principales

Multi-layer caching implementation using Memory and Redis patterns
Performance tracking against Core Web Vitals and API latency targets
Frontend bundle size reduction and rendering optimization patterns
Full-stack performance profiling for Node.js and Python environments
0 GitHub stars
Database query analysis including N+1 and JOIN optimization strategies

Casos de Uso

Optimizing inefficient database queries and implementing caching to reduce server overhead
Reducing JavaScript bundle sizes through code splitting and dependency tree shaking
Diagnosing and fixing slow page load times to improve Google Core Web Vitals

Acerca de

Características Principales

Multi-layer caching implementation using Memory and Redis patterns
Performance tracking against Core Web Vitals and API latency targets
Frontend bundle size reduction and rendering optimization patterns
Full-stack performance profiling for Node.js and Python environments
0 GitHub stars
Database query analysis including N+1 and JOIN optimization strategies

Casos de Uso

Optimizing inefficient database queries and implementing caching to reduce server overhead
Reducing JavaScript bundle sizes through code splitting and dependency tree shaking
Diagnosing and fixing slow page load times to improve Google Core Web Vitals