Performance Testing

This helps you design and run performance tests to measure response times, throughput, and resource usage under load. It covers k6 for API load testing, JMeter, pytest-benchmark for Python, JMH for Java, and database query analysis. You get working examples with realistic test stages, custom metrics, and threshold configurations. The best practices are solid: use percentiles over averages, test with realistic data volumes, and always warm up before measuring. It's opinionated about testing in production-like environments and catching N+1 queries early. Good for validating SLAs, finding bottlenecks before they hit production, or comparing performance before and after optimizations.

skills.sh installs: 609
GitHub stars: 379
last updated: Aug 21, 2026

View on GitHub

Install

$ npx -y skills add aj-geddes/useful-ai-prompts --skill performance-testing --agent claude-code

Installs into .claude/skills of the current project.

Files

Overview

Performance testing measures how systems behave under various load conditions, including response times, throughput, resource utilization, and scalability. It helps identify bottlenecks, validate performance requirements, and ensure systems can handle expected loads.

When to Use

Validating response time requirements
Measuring API throughput and latency
Testing database query performance
Identifying performance bottlenecks
Comparing algorithm efficiency
Benchmarking before/after optimizations
Validating caching effectiveness
Testing concurrent user capacity

Quick Start

Minimal working example:

// load-test.js
import http from "k6/http";
import { check, sleep } from "k6";
import { Rate, Trend } from "k6/metrics";

// Custom metrics
const errorRate = new Rate("errors");
const orderDuration = new Trend("order_duration");

SKILL.md on GitHub