Profiling & Optimization Skill

Profiling & Optimization

Overview
When to Use
Quick Start
Reference Guides
Best Practices

Overview

Profile code execution to identify performance bottlenecks and optimize critical paths using data-driven approaches.

When to Use

Performance optimization
Identifying CPU bottlenecks
Optimizing hot paths
Investigating slow requests
Reducing latency
Improving throughput

Quick Start

Minimal working example:

import { performance, PerformanceObserver } from "perf_hooks";

class Profiler {
  private marks = new Map<string, number>();

  mark(name: string): void {
    this.marks.set(name, performance.now());
  }

  measure(name: string, startMark: string): number {
    const start = this.marks.get(startMark);
    if (!start) throw new Error(`Mark ${startMark} not found`);

    const duration = performance.now() - start;
    console.log(`${name}: ${duration.toFixed(2)}ms`);

    return duration;
  }

  async profile<T>(name: string, fn: () => Promise<T>): Promise<T> {
    const start = performance.now();

    try {
      return await fn();
    } finally {
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

| Guide | Contents | |---|---| | Node.js Profiling | Node.js Profiling | | Chrome DevTools CPU Profile | Chrome DevTools CPU Profile | | Python cProfile | Python cProfile | | Benchmarking | Benchmarking | | Database Query Profiling | Database Query Profiling | | Flame Graph Generation | Flame Graph Generation |

Best Practices

✅ DO

Profile before optimizing
Focus on hot paths
Measure impact of changes
Use production-like data
Consider memory vs speed tradeoffs
Document optimization rationale

❌ DON'T

Optimize without profiling
Ignore readability for minor gains
Skip benchmarking
Optimize cold paths
Make changes without measurement

Agent Skills: Profiling & Optimization

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