Patterns and techniques for evaluating and improving AI agent outputs.
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Performance optimization and measurement for .NET applications. Navigation skill covering Span, ArrayPool, memory management, benchmarking, profiling, Native AOT, and optimization patterns. For building high-performance applications. Keywords: performance, optimization, span, arraypool, benchmarking, profiling, memory, gc, aot, native-aot
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Performance optimization and measurement for .NET applications. This meta-skill provides navigation to ~10 performance-focused skills covering zero-allocation coding, benchmarking, profiling, Native AOT, and optimization patterns.
Load this skill when:
| Need | Load Skill | Level |
|---|---|---|
| Span, ArrayPool, ref struct | dotnet-performance-patterns | Advanced |
| Type design for performance | dotnet-csharp-type-design-performance | Advanced |
| LINQ optimization | dotnet-linq-optimization | Intermediate |
| Need | Load Skill | Level |
|---|---|---|
| GC tuning, LOH/POH | dotnet-gc-memory | Advanced |
| Struct vs class decisions | dotnet-csharp-type-design-performance | Advanced |
| Need | Load Skill | Level |
|---|---|---|
| BenchmarkDotNet setup | dotnet-benchmarkdotnet | Intermediate |
| Profiling tools | dotnet-profiling | Advanced |
| CI performance gates | dotnet-ci-benchmarking | Advanced |
| Need | Load Skill | Level |
|---|---|---|
| Native AOT publishing | dotnet-native-aot | Advanced |
| AOT architecture patterns | dotnet-aot-architecture | Advanced |
| WASM AOT | dotnet-aot-wasm | Advanced |
| Trimming | dotnet-trimming | Intermediate |
| Multi-targeting | dotnet-multi-targeting | Intermediate |
| Need | Load Skill | Level |
|---|---|---|
| MAUI iOS/Catalyst AOT | dotnet-maui-aot | Advanced |
Reduce allocations → Span<T>, ArrayPool<T> (dotnet-performance-patterns)
↓
Improve throughput → Struct optimization (dotnet-csharp-type-design-performance)
↓
Reduce startup time → Native AOT (dotnet-native-aot)
↓
Optimize queries → LINQ optimization (dotnet-linq-optimization)
| Goal | Tool | Skill |
|---|---|---|
| Microbenchmarks | BenchmarkDotNet | dotnet-benchmarkdotnet |
| Live profiling | dotnet-trace | dotnet-profiling |
| Memory analysis | dotnet-counters | dotnet-profiling |
| Crash dumps | dotnet-dump | dotnet-profiling |
| CI gating | Automated benchmarks | dotnet-ci-benchmarking |
| Scenario | Recommendation |
|---|---|
| CLI tools | Strongly recommended |
| Microservices | Consider for fast startup |
| Containers | Good for size reduction |
| Web APIs | Evaluate trade-offs |
| Libraries | Use IsTrimmable |
dotnet-performance-patterns - Span, ArrayPool, ref structdotnet-csharp-type-design-performance - Struct vs class designdotnet-linq-optimization - IQueryable vs IEnumerabledotnet-gc-memory - GC tuning, LOH/POHdotnet-performance-patterns - Memory patternsdotnet-benchmarkdotnet - BenchmarkDotNetdotnet-profiling - dotnet-counters, trace, dumpdotnet-ci-benchmarking - CI performance gatingdotnet-native-aot - PublishAot, descriptorsdotnet-aot-architecture - AOT-first designdotnet-aot-wasm - Blazor/Uno WASMdotnet-trimming - Trimming annotationsdotnet-multi-targeting - Polyfills, multi-TFMdotnet-maui-aot - MAUI iOS/Catalyst optimizationdotnet-build-optimization - Slow build diagnosisdotnet-artifacts-output - UseArtifactsOutput// Use Span<T> instead of arrays
public void Process(Span<byte> data) { }
// Use ArrayPool<T> for temporary buffers
var buffer = ArrayPool<byte>.Shared.Rent(1024);
try { /* use buffer */ }
finally { ArrayPool<byte>.Shared.Return(buffer); }
// Use stackalloc for small fixed buffers
Span<int> stack = stackalloc int[100];
// Mark readonly for immutability
public readonly struct Point(double x, double y);
// Use ref readonly for large structs
public ref readonly Point GetOrigin();
// Seal classes for devirtualization
public sealed class Calculator { }
GC.TryStartNoGCRegion for critical sectionsdotnet-fundamentalsdotnet-architecturedotnet-securitydotnet-web아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Patterns and techniques for evaluating and improving AI agent outputs.
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Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations.
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Use when user requests research requiring multiple sources, comprehensive analysis, or synthesis across topics - technical research, domain knowledge gathering, market analysis, or learning about complex subjects
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AI-powered wiki generation for code repositories with commands, agents, and skills
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Use when building .NET 10 or C# 14 applications; when using minimal APIs, modular monolith patterns, or feature folders; when implementing HTTP resilience, Options pattern, Channels, or validation; when seeing outdated patterns like old extension method syntax
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Implements accessible .NET UI. SemanticProperties, ARIA, AutomationPeer, testing per platform.
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