What tools does this skill use for profiling?

It leverages industry-standard tools including py-spy and cProfile for Python, Chrome DevTools for frontend, and pg_stat_statements for PostgreSQL.

How does this skill help with slow database queries?

It provides specific protocols for N+1 query detection using SQLAlchemy, index selection strategies like HNSW and B-tree, and guides for interpreting EXPLAIN ANALYZE results.

Does it support LLM-specific optimizations?

Yes, it includes patterns for LLM response caching and semantic caching which can reduce API costs by 85-95% and significantly lower latency.

Can it help improve my Google Lighthouse scores?

Yes, it includes specialized checklists for optimizing Largest Contentful Paint (LCP), Interaction to Next Paint (INP), and Cumulative Layout Shift (CLS).

Performance Optimization

Name: Performance Optimization
Author: yonatangross

byyonatangross

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Analíticas y Monitorización

Analyzes and optimizes application performance across the full stack, including database queries, bundle sizes, and Core Web Vitals.

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The Performance Optimization skill provides a comprehensive framework for identifying and resolving bottlenecks in modern web applications. It offers production-ready patterns for backend profiling, database index selection (including HNSW for vector search), Redis caching hierarchies, and frontend optimizations like React 19 concurrent features and route-based code splitting. Whether you are troubleshooting slow API responses or aiming for perfect Core Web Vitals, this skill delivers the diagnostic tools and implementation strategies needed to scale high-traffic systems efficiently.

Características Principales

Full-stack profiling for Python backends, React frontends, and PostgreSQL databases
Automated bundle analysis and tree-shaking patterns to reduce load times
Multi-level caching strategies including Redis and LLM semantic caching
Core Web Vitals optimization protocols for LCP, INP, and CLS metrics
Advanced database tuning including N+1 query fixes and index optimization
29 GitHub stars

Casos de Uso

Reducing frontend bundle size and improving initial page load speeds for better SEO
Diagnosing and fixing slow API response times and complex database query bottlenecks
Implementing cost-effective LLM response caching and high-performance vector search

Acerca de

Características Principales

Full-stack profiling for Python backends, React frontends, and PostgreSQL databases
Automated bundle analysis and tree-shaking patterns to reduce load times
Multi-level caching strategies including Redis and LLM semantic caching
Core Web Vitals optimization protocols for LCP, INP, and CLS metrics
Advanced database tuning including N+1 query fixes and index optimization
29 GitHub stars

Casos de Uso

Reducing frontend bundle size and improving initial page load speeds for better SEO
Diagnosing and fixing slow API response times and complex database query bottlenecks
Implementing cost-effective LLM response caching and high-performance vector search