Can this skill help with database N+1 problems?

Yes, it is specifically designed to detect inefficient data fetching patterns and provide refactored code using eager loading or batching to minimize database roundtrips.

How does the Performance Optimizer identify bottlenecks?

The skill follows a 'measure-first' approach, utilizing profiling tools and metrics like LCP, query execution times, and CPU usage to pinpoint exact sources of latency before suggesting optimizations.

Can I use this for load testing?

Yes, the skill provides guidance on setting up performance baselines and executing stress tests using tools like k6 to ensure your system can handle production loads.

Does it support frontend bundle size reduction?

Absolutely. It analyzes dependencies and suggests strategies like code splitting, lazy loading, and tree-shaking to significantly reduce JavaScript delivery times.

Performance Optimizer

Name: Performance Optimizer
Author: anton-abyzov

byanton-abyzov

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Analíticas y Monitorización

Optimizes full-stack application performance through profiling, database tuning, and frontend efficiency strategies.

Acerca de

The Performance Optimizer skill transforms Claude into a senior performance engineer capable of diagnosing and resolving bottlenecks across the entire software stack. It provides specialized guidance on improving Core Web Vitals, refactoring inefficient database queries, implementing multi-level caching strategies, and reducing frontend bundle sizes. By prioritizing a 'measure-first' methodology, this skill helps developers identify the 20% of code causing 80% of latency, ensuring that production applications are scalable, responsive, and resource-efficient.

Características Principales

Automated load testing and performance benchmarking with k6
Distributed caching implementation using Redis and CDN strategies
23 GitHub stars
Comprehensive frontend optimization for Core Web Vitals (LCP, FID, CLS)
Database query profiling and automated N+1 problem resolution
Backend latency reduction via connection pooling and async processing

Casos de Uso

Scaling high-traffic APIs to handle thousands of concurrent requests
Reducing infrastructure costs by fixing memory leaks and high CPU usage
Improving Google Search rankings by optimizing Core Web Vitals scores

Acerca de

Características Principales

Automated load testing and performance benchmarking with k6
Distributed caching implementation using Redis and CDN strategies
23 GitHub stars
Comprehensive frontend optimization for Core Web Vitals (LCP, FID, CLS)
Database query profiling and automated N+1 problem resolution
Backend latency reduction via connection pooling and async processing

Casos de Uso

Scaling high-traffic APIs to handle thousands of concurrent requests
Reducing infrastructure costs by fixing memory leaks and high CPU usage
Improving Google Search rankings by optimizing Core Web Vitals scores