perf

Profile and optimize performance bottlenecks in Python, Rust, and web applications.

4|1|Updated Jan 27, 2026
One-click install
npx skills add https://github.com/AreteDriver/ai_skills --skill perf-aretedriver
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: perf
Source: https://github.com/AreteDriver/ai_skills/tree/main/skills/perf
Command: npx skills add https://github.com/AreteDriver/ai_skills --skill perf-aretedriver

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill enables profiling and optimization of performance bottlenecks in Python, Rust, and web applications, turning slow systems into fast, reliable software.

Core Features & Use Cases

  • Baseline measurement and bottleneck identification across languages and HTTP endpoints.
  • Language-specific profiling with guidance on CPU, memory, and I/O hotspots.
  • Verification of improvements through before/after benchmarking and observable metrics.

Quick Start

Start by establishing a baseline for your Python, Rust, and web components, then iterate on targeted optimizations and validate gains.

Frequently Asked Questions about perf

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I find performance bottlenecks in Python and Rust applications?

Web performance profiling benchmarks HTTP endpoints by establishing baseline measurements across network requests. This identifies slow endpoints and validates improvements through rigorous before/after benchmarking and observable metrics.

How do I benchmark web performance for HTTP endpoints?

Web performance profiling benchmarks HTTP endpoints by establishing baseline measurements across network requests. This identifies slow endpoints and validates improvements through rigorous before/after benchmarking and observable metrics.

What is the best way to validate performance improvements after optimizing code?

Profiling Rust applications isolates CPU and memory hotspots using language-specific profiling tools to capture execution baselines. This targets development workflows requiring bottleneck identification and validated performance improvements.

How do I profile Rust applications to identify memory and CPU hotspots?

Profiling Rust applications isolates CPU and memory hotspots using language-specific profiling tools to capture execution baselines. This targets development workflows requiring bottleneck identification and validated performance improvements.

Can I use profiling to optimize both Python and web components in the same workflow?

Profiling can optimize Python and web components in the same workflow by establishing baselines for both, then iterating on targeted optimizations. It validates gains across mixed languages and HTTP endpoints using before/after benchmarking.