profiling-optimization

SKILL.md

Profiling & Optimization

Table of Contents

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

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