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m10-performance

Identify and mitigate performance bottlenecks in Rust projects with profiling and benchmarking.

Updated Feb 8, 2026
Search Tags:#performance#profiling#cache#rust#optimize#benchmarks#micro-optimizations
One-click install
npx skills add https://github.com/yumazak/kodo --skill m10-performance-yumazak
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: m10-performance
Source: https://github.com/yumazak/kodo/tree/main/.agents/skills/m10-performance
Command: npx skills add https://github.com/yumazak/kodo --skill m10-performance-yumazak

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Identifies and mitigates performance bottlenecks in Rust applications, helping developers measure, reason about, and improve execution speed and resource efficiency.

Core Features & Use Cases

  • Guided optimization workflow with measurable goals aligned to real-world performance needs
  • Profiling, benchmarking, and memory-management guidance to locate hotspots
  • Practical techniques such as pre-allocation, cache-friendly data layouts, and parallelism where safe

Quick Start

Analyze a Rust project to locate bottlenecks, suggest targeted optimizations, and provide a step-by-step plan.

Frequently Asked Questions about m10-performance

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

FAQPage Schema
How do I optimize Rust performance and reduce CPU bottlenecks?▼

To optimize Rust performance, you must profile CPU-bound workloads using flamegraphs and criterion benchmarks to locate hotspots, then apply memory optimizations like reducing allocations and improving cache locality. This provides an actionable step-by-step plan to mitigate bottlenecks.

What is the best way to improve cache locality in Rust applications?▼

Improving cache locality involves restructuring data layouts to be cache-friendly and pre-allocating memory. This technique minimizes expensive allocations and improves execution speed by ensuring CPU-bound workloads access memory more efficiently.

How do I use criterion benchmarks to measure Rust micro-optimizations?▼

You use criterion benchmarks to establish measurable goals and validate micro-optimizations. By profiling before and after applying techniques like pre-allocation or parallelism, you ensure execution speed improvements align with real-world performance needs.

Can I apply these profiling and optimization tactics to systems similar to Rust?▼

Yes, these profiling and optimization tactics apply to CPU-bound workloads across Rust projects and similar systems. The guided workflow focuses on measuring, reasoning about, and improving execution speed and resource efficiency safely.

When should I use safe parallelism to speed up my Rust app?▼

You should implement safe parallelism when optimizing CPU-bound workloads after identifying bottlenecks via profiling. Parallelizing work complements other techniques like pre-allocation and cache-friendly data layouts to maximize execution speed.

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