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performance-engineer

Diagnose and optimize application performance using observability, profiling, and load testing.

2|Updated Jun 16, 2026
Search Tags:#caching#scalability#performance optimization#profiling#observability#load testing#opentelemetry
One-click install
npx skills add https://github.com/monang404/lunawave --skill performance-engineer-monang404
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: performance-engineer
Source: https://github.com/monang404/lunawave/tree/main/.agent/skills/performance-engineer
Command: npx skills add https://github.com/monang404/lunawave --skill performance-engineer-monang404

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? It helps engineers find and fix performance bottlenecks across backend, frontend, and infrastructure by combining profiling, distributed tracing, load testing, and monitoring into a structured optimization workflow. ## Core Features & Use Cases - Observability Setup: Configure OpenTelemetry, Prometheus, Grafana, and APM platforms for distributed tracing, metrics, and SLI/SLO tracking. - Bottleneck Analysis: Use CPU, memory, and I/O profiling with flame graphs and query analysis to isolate hotspots across services and databases. - Load Testing & Caching: Design k6/JMeter/Gatling load tests and multi-tier caching strategies (Redis, CDN, browser) with performance budgets to prevent regressions. - Use Case: An API's p95 latency spikes under traffic. Use this Skill to trace requests with OpenTelemetry, profile the slow database queries, add Redis caching, and validate the fix with a k6 load test. ## Quick Start Analyze the performance of my API endpoints, identify the biggest bottlenecks, and propose optimizations with expected impact.

Frequently Asked Questions about performance-engineer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I find performance bottlenecks in my application?▼

Start by establishing a baseline with metrics and profiling, then use distributed tracing (OpenTelemetry) and flame graphs to isolate hotspots. Prioritize the biggest bottlenecks by user impact before optimizing.

What load testing tools should I use for API performance testing?▼

k6, JMeter, Gatling, Locust, and Artillery are common choices for API load testing. Use realistic traffic patterns and production-like data, and integrate tests into CI/CD with performance budgets to catch regressions.

How do I set up observability with OpenTelemetry and Prometheus?▼

Instrument services with OpenTelemetry for distributed tracing and metrics, export to Prometheus for storage, and visualize with Grafana dashboards. Add SLI/SLO tracking and alerting for continuous performance monitoring.

Does this approach work for frontend performance optimization?▼

Yes, it covers Core Web Vitals (LCP, FID, CLS), bundle splitting, lazy loading, critical CSS, and CDN caching. Real User Monitoring tracks actual user experience rather than relying only on synthetic benchmarks.

When should I avoid running load tests?▼

Avoid load testing production environments without explicit approvals and safeguards. Use staging environments, staged rollouts, and rollback plans for high-risk changes to prevent user-facing disruption.

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