Profiling optimization

Skill ulpi-io/plugin-marketplace/plugins/aj-geddes/skills/profiling-optimization

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Install

npx -y skills add ulpi-io/plugin-marketplace --skill profiling-optimization

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What its author says it does

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Profile application performance, identify bottlenecks, and optimize hot paths using CPU profiling, flame graphs, and benchmarking. Use when investigating performance issues or optimizing critical code paths.

SKILL.md

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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