Profiling Optimization

This one walks you through finding performance bottlenecks using CPU profiling, flame graphs, and benchmarking across multiple languages. You get working examples for Node.js perf_hooks, Chrome DevTools profiling, Python's cProfile, and database query analysis. The approach is sensible: profile first, focus on hot paths, measure everything. What I like is it includes both the instrumentation code and the tooling to actually visualize what's slow, not just raw timing data. The best practices are refreshingly pragmatic about not optimizing cold paths or sacrificing readability for negligible gains. Solid reference if you're investigating slow requests or need to justify optimization work with real data instead of hunches.

skills.sh installs: 456
GitHub stars: 379
last updated: Aug 21, 2026

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Install

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

Installs into .claude/skills of the current project.

Files

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();

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