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performance

Analyze and optimize RTK CLI startup time, binary size, and memory usage.

75.3k|4.7k|Updated Jan 22, 2026
Search Tags:#performance#optimization#benchmarking#memory usage#binary size#startup time#rtk cli
One-click install
npx skills add https://github.com/rtk-ai/rtk --skill performance-rtk-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: performance
Source: https://github.com/rtk-ai/rtk/tree/main/.claude/skills/performance
Command: npx skills add https://github.com/rtk-ai/rtk --skill performance-rtk-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses performance bottlenecks in the RTK CLI, ensuring fast startup times, minimal binary size, and efficient memory usage.

Core Features & Use Cases

  • Performance Benchmarking: Measure and optimize startup time, binary size, and memory footprint.
  • Dependency Analysis: Assess the performance impact of adding new dependencies.
  • Code Optimization: Identify and fix performance anti-patterns like regex recompilation and unnecessary allocations.
  • Use Case: Before merging a code change that might affect speed, use this Skill to benchmark startup time and ensure it remains below the 10ms target.

Quick Start

Benchmark the startup time of the rtk git status command using hyperfine.

Frequently Asked Questions about performance

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

FAQPage Schema
How do I benchmark CLI startup time to ensure it meets performance targets?▼

Benchmark CLI startup time using hyperfine to measure execution speed and verify it remains below the 10ms target. This methodology accurately captures baseline performance metrics before merging code changes.

What is the best way to analyze binary size bloat in a Rust CLI?▼

Analyze binary size bloat using cargo bloat to identify large dependencies and unnecessary code paths. This process highlights specific functions consuming binary space, enabling targeted size optimization.

How do I assess the performance impact of adding a new dependency?▼

Assess dependency impact by profiling memory usage and startup time before and after integration. This analysis identifies performance regressions introduced by new library additions.

How does profiling identify memory usage bottlenecks in command line tools?▼

Profiling identifies memory usage bottlenecks by tracking allocations and runtime memory footprint during execution. This reveals inefficient allocation patterns and memory leaks affecting CLI performance.

Why does recompiling regex patterns cause CLI performance regressions?▼

Recompiling regex patterns causes performance regressions by wasting CPU cycles on repeated compilation instead of reusing compiled instances. Identifying this anti-pattern eliminates unnecessary allocations and speeds up execution.

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