Does it support modern Ruby concurrency?

Absolutely. The skill includes implementation guides for Ruby 3 Ractors for CPU-bound tasks and the Async gem for I/O-bound concurrency.

When should I use benchmark-ips over standard benchmarking?

The skill recommends benchmark-ips when you need statistically significant results and easy-to-read comparison metrics between different code implementations.

Can this skill help reduce Ruby memory usage?

Yes, it provides specific patterns for using frozen string literals, lazy enumerables, and object pooling to significantly reduce memory overhead.

How do I identify bottlenecks in my Ruby code?

This skill guides you through using stackprof for CPU profiling and memory_profiler for object allocation analysis to pinpoint exactly where your code is slow.

Ruby Performance Optimization

Name: Ruby Performance Optimization
Author: bastos

bybastos

Analytics & Monitoring

Optimizes Ruby codebases through profiling, benchmarking, and advanced memory management techniques.

About

This skill provides expert guidance on identifying and resolving performance bottlenecks in Ruby applications by leveraging industry-standard tools and best practices. It covers essential profiling with stackprof, statistical benchmarking with benchmark-ips, and memory analysis to reduce object allocations. Users can access implementation patterns for lazy enumerables, frozen string literals, garbage collection tuning, and modern concurrency models like Ractors and Async, making it an indispensable resource for building high-performance Ruby software.

Key Features

Concurrency implementation for Threads, Ractors, and Async
Statistical benchmarking patterns with benchmark-ips
Interactive profiling guidance using stackprof and memory_profiler
Memory optimization techniques including object pooling and frozen strings
Garbage Collection (GC) analysis and environment variable tuning
0 GitHub stars

Use Cases

Comparing multiple algorithmic approaches for optimal execution speed
Identifying and fixing CPU bottlenecks in slow Ruby methods
Reducing memory footprints in data-heavy Ruby applications

About

Key Features

Concurrency implementation for Threads, Ractors, and Async
Statistical benchmarking patterns with benchmark-ips
Interactive profiling guidance using stackprof and memory_profiler
Memory optimization techniques including object pooling and frozen strings
Garbage Collection (GC) analysis and environment variable tuning
0 GitHub stars

Use Cases

Comparing multiple algorithmic approaches for optimal execution speed
Identifying and fixing CPU bottlenecks in slow Ruby methods
Reducing memory footprints in data-heavy Ruby applications