M10 performance
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CRITICAL: Use for performance optimization. Triggers: performance, optimization, benchmark, profiling, flamegraph, criterion, slow, fast, allocation, cache, SIMD, make it faster, ζ§θ½δΌε, εΊεζ΅θ―
SKILL.md
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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 β 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 β flamegraph, perf
- Benchmark β 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 β
To domain constraints (Layer 3):
"How fast does this need to be?"
β Ask: What's the performance SLA?
β Check: domain-* (latency requirements)
β 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 β
To implementation (Layer 1):
"Need to reduce allocations"
β m01-ownership: Use references, avoid clone
β m02-resource: Pre-allocate with_capacity
"Need to parallelize"
β m07-concurrency: Choose rayon or threads
β m07-concurrency: Consider async for I/O-bound
"Need cache efficiency"
β Data layout: Prefer Vec over HashMap when possible
β 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-* |
What ships with it: 1 file
6.9 KB alongside SKILL.md
patterns/
- optimization-guide.md6.9 KB
Gives 0 of the 12 instructions most performance cost skills give in 982 tokens
Counted across 803 of the 1,058 authors here whose files we hold, read 2026-08-07
- keep skill files under 500 linesin 82 of 803, across 16 files
- use imperative form in instructionsin 80 of 803, across 9 files
- draft assertions while test runs are in progressin 75 of 803, across 9 files
- create two to three realistic test promptsin 74 of 803, across 9 files
- write skill descriptions to be pushyin 72 of 803, across 7 files
- save test cases to evals jsonin 72 of 803, across 6 files
- ask questions about edge cases and input formatsin 72 of 803, across 7 files
- save timing data immediately when runs completein 70 of 803, across 5 files
- include all trigger conditions in the skill descriptionin 69 of 803, across 3 files
- launch all test runs in a single turnin 69 of 803, across 3 files
- capture intent before writing a skillin 67 of 803, across 1 file
- import directly instead of barrel filesin 52 of 803, across 15 files
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.