Can this skill be used safely on production systems?

Yes, it includes specific safety protocols and instructions for gradual rollouts, rollback plans, and requires explicit approval before running load tests on production infrastructure.

Which performance metrics does this skill focus on?

It targets critical metrics including Core Web Vitals (LCP, FID, CLS), API response times (P50/P95/P99), database query latency, and overall system throughput.

Does it support frontend-specific optimizations?

Absolutely. It handles code splitting, tree shaking, lazy loading, resource hinting, and CDN/Edge configuration to improve the critical rendering path.

What tools are compatible with this performance workflow?

The skill is designed to work with popular tools such as DataDog, New Relic, Redis, k6, Prometheus, Grafana, and standard cloud provider monitoring suites.

Full-Stack Performance Optimizer

Name: Full-Stack Performance Optimizer
Author: JantonioFC

byJantonioFC

•

分析と監視

Optimizes end-to-end application performance through deep profiling, database tuning, and frontend orchestration across the entire technical stack.

The Full-Stack Performance Optimizer is a specialized skill designed to identify and resolve performance bottlenecks across every layer of your application. It provides a structured workflow that starts with establishing data-driven baselines using profiling and observability tools, then moves into targeted optimizations for databases, backend microservices, and frontend delivery. By coordinating specialized sub-agents for performance engineering and infrastructure, it ensures that improvements are validated through rigorous load testing and protected against future regressions with automated performance budgets and monitoring guardrails.

主な機能

01Full observability setup with OpenTelemetry and APM integration

02Frontend Core Web Vitals tuning and asset delivery optimization

03Database query optimization and caching strategy implementation

042 GitHub stars

05Comprehensive CPU and memory profiling with flame graph generation

06Automated load testing using industry-standard tools like k6 and Artillery

ユースケース

01Diagnosing and fixing intermittent latency issues in distributed microservices

02Scaling existing applications to handle significant increases in user traffic

03Setting up performance regression testing within CI/CD pipelines

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jantoniofc/skillsbank application-performance-performance-optimization

For use in Claude.ai and ChatGPT

主な機能

01Full observability setup with OpenTelemetry and APM integration

02Frontend Core Web Vitals tuning and asset delivery optimization

03Database query optimization and caching strategy implementation

042 GitHub stars

05Comprehensive CPU and memory profiling with flame graph generation

06Automated load testing using industry-standard tools like k6 and Artillery

ユースケース

01Diagnosing and fixing intermittent latency issues in distributed microservices

02Scaling existing applications to handle significant increases in user traffic

03Setting up performance regression testing within CI/CD pipelines

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jantoniofc/skillsbank application-performance-performance-optimization

For use in Claude.ai and ChatGPT