Profiling Optimization

aj-geddes/useful-ai-prompts/skills/profiling-optimization

by aj-geddes3f5182cfd739No license355 starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated 7 months ago

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.

Includes scriptsSoftware Development Data & Analytics

AI-generated overview

Profiles application performance to find bottlenecks and optimize hot paths using CPU profiling, flame graphs, and benchmarking.

What it does: Guides an agent through profiling code execution to locate performance bottlenecks and optimize critical paths with a data-driven approach. It covers Node.js profiling, Chrome DevTools CPU profiles, Python cProfile, benchmarking, database query profiling, and flame graph generation. It also ships a validation script and an API scaffold template.
When to use it: Use it when investigating performance issues, identifying CPU bottlenecks, optimizing hot paths, or reducing latency and improving throughput.
Requirements: Runs executable scripts (scripts/validate-api.sh) and reads reference guides and a template. Profiling targets require the relevant runtimes and tools, such as Node.js, Python with cProfile, Chrome DevTools, and database access for query profiling.

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:

typescript

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)

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 [blocked]	Node.js Profiling
Chrome DevTools CPU Profile [blocked]	Chrome DevTools CPU Profile
Python cProfile [blocked]	Python cProfile
Benchmarking [blocked]	Benchmarking
Database Query Profiling [blocked]	Database Query Profiling
Flame Graph Generation [blocked]	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

Source and attribution

Source:aj-geddes/useful-ai-promptsinskills/profiling-optimizationat commit3f5182c

License: No license

Content belongs to its original authors. SourceWeft indexes it from a public repository.

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Profiling Optimization

Profiling & Optimization

Table of Contents

Overview

When to Use

Quick Start

Reference Guides

Best Practices

✅ DO

❌ DON'T

Source and attribution

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