m10-performance
CRITICAL: Use for performance optimization. Triggers: performance, optimization, benchmark, profiling, flamegraph, criterion, slow, fast, allocation, cache, SIMD, make it faster, 性能优化, 基准测试
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/actionbook/rust-skills --skill m10-performance- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads m10-performance automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
Trace Up ↑
To domain constraints (Layer 3):
"How fast does this need to be?"
↑ Ask: What's the performance SLA?
↑ Check: domain-* (latency requirements)
↑ Check: Business requirements (acceptable response time)
| Question | Trace To | Ask |
|---|---|---|
| Latency requirements | domain-* | What's acceptable response time? |
| Throughput needs | domain-* | How many requests per second? |
| Memory constraints | domain-* | What's the memory budget? |
Trace Down ↓
To implementation (Layer 1):
"Need to reduce allocations"
↓ m01-ownership: Use references, avoid clone
↓ m02-resource: Pre-allocate with_capacity
"Need to parallelize"
↓ m07-concurrency: Choose rayon or threads
↓ m07-concurrency: Consider async for I/O-bound
"Need cache efficiency"
↓ Data layout: Prefer Vec over HashMap when possible
↓ Access patterns: Sequential over random access
Quick Reference
| Tool | Purpose |
|---|---|
cargo bench | Micro-benchmarks |
criterion | Statistical benchmarks |
perf / flamegraph | CPU profiling |
heaptrack | Allocation tracking |
valgrind / cachegrind | Cache analysis |
Optimization Priority
1. Algorithm choice (10x - 1000x)
2. Data structure (2x - 10x)
3. Allocation reduction (2x - 5x)
4. Cache optimization (1.5x - 3x)
5. SIMD/Parallelism (2x - 8x)
Common Techniques
| Technique | When | How |
|---|---|---|
| Pre-allocation | Known size | Vec::with_capacity(n) |
| Avoid cloning | Hot paths | Use references or Cow<T> |
| Batch operations | Many small ops | Collect then process |
| SmallVec | Usually small | smallvec::SmallVec<[T; N]> |
| Inline buffers | Fixed-size data | Arrays over Vec |
Common Mistakes
| Mistake | Why Wrong | Better |
|---|---|---|
| Optimize without profiling | Wrong target | Profile first |
| Benchmark in debug mode | Meaningless | Always --release |
| Use LinkedList | Cache unfriendly | Vec or VecDeque |
Hidden .clone() | Unnecessary allocs | Use references |
| Premature optimization | Wasted effort | Make it work first |
Anti-Patterns
| Anti-Pattern | Why Bad | Better |
|---|---|---|
| Clone to avoid lifetimes | Performance cost | Proper ownership |
| Box everything | Indirection cost | Stack when possible |
| HashMap for small sets | Overhead | Vec with linear search |
| String concat in loop | O(n^2) | String::with_capacity or format! |
Related Skills
| When | See |
|---|---|
| Reducing clones | m01-ownership |
| Concurrency options | m07-concurrency |
| Smart pointer choice | m02-resource |
| Domain requirements | domain-* |
FAQ
What does the m10-performance skill do?
CRITICAL: Use for performance optimization. Triggers: performance, optimization, benchmark, profiling, flamegraph, criterion, slow, fast, allocation, cache, SIMD, make it faster, 性能优化, 基准测试
How do I install m10-performance?
Run npx skills add https://github.com/actionbook/rust-skills --skill m10-performance in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/m10-performance/ (personal) or .claude/skills/m10-performance/ (project).
Which AI agents can use m10-performance?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is m10-performance safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains m10-performance?
It is published by actionbook in actionbook/rust-skills. It has 3,623 recorded installs.
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