Does this skill help with Rust benchmarking?

Yes, it provides specific guidance for using tools like Criterion and cargo-bench to measure performance accurately and avoid common benchmarking mistakes.

Can it help reduce memory allocations in Rust?

Absolutely. It includes patterns for pre-allocation, reusing buffers, and using specialized types like SmallVec or Cow to minimize heap usage.

When should I trigger the m10-performance skill?

Trigger this skill whenever you encounter slow code, high memory usage, or when you are preparing for production deployment and need to establish performance baselines.

Does it suggest specific Rust crates for optimization?

Yes, it recommends industry-standard crates such as Rayon for parallelism, Criterion for statistical benchmarking, and SmallVec for stack-based optimizations.

What is the recommended optimization priority?

The skill prioritizes algorithm choice first, followed by data structure selection, allocation reduction, and finally cache and SIMD optimizations for the greatest impact.

Rust Performance Optimizer

Name: Rust Performance Optimizer
Author: actionbook

byactionbook

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596

Developer Tools

Optimizes Rust application performance through systematic profiling, memory management, and efficient data structure selection.

About

The m10-performance skill provides a comprehensive framework for identifying and resolving performance bottlenecks in Rust projects. It guides developers through a disciplined 'measure-first' approach using industry-standard tools like Criterion and Flamegraphs, offering specific implementation patterns for reducing allocations, improving cache locality, and leveraging parallelism. By integrating performance Service Level Agreements (SLAs) with low-level implementation choices, it ensures that optimizations are both effective and maintainable while avoiding common pitfalls like premature optimization or cache-unfriendly data structures.

Key Features

Systematic profiling and benchmarking guidance using Criterion and cargo-bench
Algorithmic and data structure trade-off analysis for maximum throughput
Memory optimization strategies including pre-allocation and zero-copy patterns
Identification of performance anti-patterns like unnecessary cloning or boxing
Cache-efficient data layout recommendations to minimize CPU stalls
596 GitHub stars

Use Cases

Reducing latency in high-throughput backend services and performance-critical APIs
Optimizing memory usage and allocation patterns in resource-constrained environments
Refactoring hot paths in computation-heavy Rust libraries using SIMD or parallelism

About

Key Features

Systematic profiling and benchmarking guidance using Criterion and cargo-bench
Algorithmic and data structure trade-off analysis for maximum throughput
Memory optimization strategies including pre-allocation and zero-copy patterns
Identification of performance anti-patterns like unnecessary cloning or boxing
Cache-efficient data layout recommendations to minimize CPU stalls
596 GitHub stars

Use Cases

Reducing latency in high-throughput backend services and performance-critical APIs
Optimizing memory usage and allocation patterns in resource-constrained environments
Refactoring hot paths in computation-heavy Rust libraries using SIMD or parallelism