Can I automate performance checks in my pipeline?

Absolutely. The skill includes GitHub Actions examples and performance budget configurations to catch regressions during CI/CD.

How does this skill assist with frontend SEO?

It includes specific optimization strategies for Core Web Vitals, such as Largest Contentful Paint (LCP) and Cumulative Layout Shift (CLS), to improve search engine rankings.

What load testing tools does this skill support?

The skill provides comprehensive implementation patterns and quick-start guides for k6 (JavaScript) and Locust (Python).

Which programming environments are covered?

It provides tailored profiling and optimization workflows for Python, Go, and Node.js/TypeScript environments.

Is it safe to use these profiling techniques in production?

Yes, it emphasizes the use of sampling profilers like py-spy and pprof, which are designed for low overhead and safe production debugging.

Performance Engineering

Name: Performance Engineering
Author: ancoleman

byancoleman

•

158

Security & Testing

Optimizes system responsiveness and scalability through comprehensive load testing, deep profiling, and full-stack tuning strategies.

About

The Performance Engineering skill provides a robust framework for validating system capacity and resolving resource bottlenecks across the entire application stack. It equips developers with standardized patterns for load, stress, and soak testing using tools like k6 and Locust, alongside deep profiling techniques for CPU, memory, and I/O. By integrating performance SLOs, database query optimization, and Core Web Vitals improvements, this skill ensures applications remain fast, reliable, and cost-effective under varying traffic volumes.

Key Features

Frontend Core Web Vitals tuning for LCP, INP, and CLS metrics
Database and API optimization strategies including N+1 prevention and caching
158 GitHub stars
Production-safe profiling workflows for Python, Go, and Node.js
Standardized load, stress, soak, and spike testing patterns using k6 and Locust
Automated performance budgets and CI/CD integration frameworks

Use Cases

Validating system capacity and auto-scaling limits before high-traffic product launches
Identifying and fixing memory leaks or resource exhaustion in long-running services
Detecting performance regressions automatically within automated deployment pipelines

About

Key Features

Frontend Core Web Vitals tuning for LCP, INP, and CLS metrics
Database and API optimization strategies including N+1 prevention and caching
158 GitHub stars
Production-safe profiling workflows for Python, Go, and Node.js
Standardized load, stress, soak, and spike testing patterns using k6 and Locust
Automated performance budgets and CI/CD integration frameworks

Use Cases

Validating system capacity and auto-scaling limits before high-traffic product launches
Identifying and fixing memory leaks or resource exhaustion in long-running services
Detecting performance regressions automatically within automated deployment pipelines