M10 Performance

The m10-performance skill guides developers through systematic performance optimization by establishing whether optimization is necessary before implementing solutions. It maps performance goals—such as reducing allocations, improving cache efficiency, and parallelizing workloads—to specific design choices and implementation techniques in Rust, while emphasizing measurement through profiling and benchmarking tools like criterion and flamegraph. The skill traces constraints from business requirements down to low-level implementation decisions, helping developers identify actual bottlenecks and understand the complexity-versus-speed trade-offs involved in optimization.

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last updated: Jul 14, 2026

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Install

$ npx -y skills add zhanghandong/rust-skills --skill m10-performance --agent claude-code

Installs into .claude/skills of the current project.

Files

Performance Optimization

Layer 2: Design Choices

Core Question

What's the bottleneck, and is optimization worth it?

Before optimizing:

Have you measured? (Don't guess)
What's the acceptable performance?
Will optimization add complexity?

Performance Decision → Implementation

Goal	Design Choice	Implementation
Reduce allocations	Pre-allocate, reuse	`with_capacity`, object pools
Improve cache	Contiguous data	`Vec`, `SmallVec`
Parallelize	Data parallelism	`rayon`, threads
Avoid copies	Zero-copy	References, `Cow<T>`
Reduce indirection	Inline data	`smallvec`, arrays

Thinking Prompt

Before optimizing:

Have you measured?
- Profile first → flamegraph, perf
- Benchmark → criterion, cargo bench
- Identify actual hotspots
What's the priority?
- Algorithm (10x-1000x improvement)
- Data structure (2x-10x)
- Allocation (2x-5x)
- Cache (1.5x-3x)
What's the trade-off?
- Complexity vs speed
- Memory vs CPU
- Latency vs throughput