performance-profiler

Profile code to identify hot paths and algorithmic inefficiencies.

2|Updated Feb 3, 2026

Search Tags:#performance #optimization #benchmarking #profiling #latency #bottlenecks #code-efficiency

Ready to run

npx skills add https://github.com/k1lgor/virtual-company --skill performance-profiler-k1lgor

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: performance-profiler
Source: https://github.com/k1lgor/virtual-company/tree/main/skills/06-performance-profiler
Command: npx skills add https://github.com/k1lgor/virtual-company --skill performance-profiler-k1lgor

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Use this skill to identify performance bottlenecks, eliminate unnecessary work, and optimize algorithms to make code faster and more efficient.

Core Features & Use Cases

Profile hot paths and bottlenecks in functions, loops, and I/O-bound sections.
Recommend practical improvements (algorithmic tweaks, caching strategies, and parallelism) with concrete before/after guidance.
Use existing benchmarks or add simple timing checks to verify improvements in real projects.

Quick Start

Run a profiling pass on the target function to identify bottlenecks.

Frequently Asked Questions about performance-profiler

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

FAQPage Schema

How do I identify performance bottlenecks and hot paths in my code?▼

Run a profiling pass on your target function to discover hot paths and unnecessary work. This bottleneck discovery process clarifies constraints and exposes algorithmic inefficiencies within loops and I/O operations for targeted optimization.

What is the best way to profile code latency and find optimization opportunities?▼

Profile code across functions, loops, and I/O-bound sections to pinpoint latency bottlenecks. This guides constraint clarification and exposes algorithmic inefficiencies to uncover concrete improvement opportunities for code optimization.

How do I verify code efficiency improvements after optimizing an algorithm?▼

Verify code efficiency improvements by checking before/after benchmarks. Use existing benchmarks or add simple timing checks to your real projects to confirm that algorithmic tweaks, caching strategies, and parallelism actually reduced latency.

Can I use this approach to profile I/O-bound sections and recommend caching strategies?▼

Yes, you can profile I/O-bound sections to identify bottlenecks and unnecessary work. The profiling process then recommends practical improvements like caching strategies, algorithmic tweaks, and parallelism with concrete before/after guidance.

What types of practical improvements does code profiling recommend for slow functions?▼

Code profiling recommends practical improvements like algorithmic tweaks, caching strategies, and parallelism for slow functions. It provides concrete before/after guidance to help eliminate unnecessary work and speed up code.

Why does profiling hot paths require clarifying constraints before benchmarking?▼

Profiling hot paths requires clarifying constraints to accurately target bottleneck discovery and avoid optimizing unnecessary work. This ensures before/after benchmarks verify actual code efficiency improvements in real projects.