Patterns and techniques for evaluating and improving AI agent outputs.
日本語の概要は準備中です。原文の説明を表示しています。
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
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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-webまだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Patterns and techniques for evaluating and improving AI agent outputs.
日本語の概要は準備中です。原文の説明を表示しています。
Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations.
日本語の概要は準備中です。原文の説明を表示しています。
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
日本語の概要は準備中です。原文の説明を表示しています。
AI-powered wiki generation for code repositories with commands, agents, and skills
日本語の概要は準備中です。原文の説明を表示しています。
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
日本語の概要は準備中です。原文の説明を表示しています。
Implements accessible .NET UI. SemanticProperties, ARIA, AutomationPeer, testing per platform.
日本語の概要は準備中です。原文の説明を表示しています。