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GitHub リポジトリ

dotnet-artisan

dotnet-artisan には fenzel999 から収集した 11 個の skills があり、リポジトリ単位の職業カバレッジとサイト内 skill 詳細ページを表示します。

収集済み skills
11
Stars
7
更新
2026-05-31
Forks
0
職業カバレッジ
3 件の職業カテゴリ · 100% 分類済み
リポジトリエクスプローラー

このリポジトリの skills

dotnet-advisor
ソフトウェア開発者

Central orchestrator for ALL .NET development requests. Owns the entire flow: detects .NET version, aligns requirements via structured questioning (USAGE.md framework), loads coding standards, applies .NET-first defaults, and routes to the correct domain skills and specialist agents. Invoked after [skill:using-dotnet] confirms .NET intent. The single entry point for every .NET task — no request should bypass this skill. Do not use for domain-specific implementation guidance.

2026-05-31
dotnet-api
ソフトウェア開発者

Builds ASP.NET Core APIs, EF Core data access, gRPC, SignalR, and backend services with middleware, security (OAuth, JWT, OWASP), resilience, messaging, OpenAPI, .NET Aspire, Semantic Kernel, HybridCache, YARP reverse proxy, output caching, Office documents (Excel, Word, PowerPoint), PDF, and architecture patterns. Spans 33 topic areas. Do not use for UI rendering patterns or CI/CD pipeline authoring.

2026-05-31
dotnet-csharp
ソフトウェア開発者

Baseline C# skill loaded for every .NET code path. Guides language patterns (records, pattern matching, primary constructors, C# 8-15), coding standards, async/await, DI, LINQ, serialization, domain modeling, concurrency, Roslyn analyzers, globalization, native interop (P/Invoke, LibraryImport, ComWrappers), WASM interop (JSImport/JSExport), and type design. Spans 27 topics. Do not use for ASP.NET endpoint architecture, UI framework patterns, or CI/CD guidance.

2026-05-31
dotnet-debugging
ソフトウェア開発者

Debugs Windows and Linux/macOS applications (native, .NET/CLR, mixed-mode) with WinDbg MCP (crash dumps, !analyze, !syncblk, !dlk, !runaway, !dumpheap, !gcroot, BSOD), dotnet-dump, lldb with SOS, createdump, and container diagnostics (Docker, Kubernetes). Hang/deadlock diagnosis, high CPU triage, memory leak investigation, kernel debugging, and dotnet-monitor for production. Spans 17 topic areas. Do not use for routine .NET SDK profiling, benchmark design, or CI test debugging.

2026-05-31
dotnet-devops
ソフトウェア開発者

Configures .NET CI/CD pipelines (GitHub Actions with setup-dotnet, NuGet cache, reusable workflows; Azure DevOps with DotNetCoreCLI, templates, multi-stage), Git workflow (branch strategies, Conventional Commits, PR lifecycle), containerization (multi-stage Dockerfiles, Compose, rootless), packaging (NuGet authoring, source generators, MSIX signing), release management (NBGV, SemVer, changelogs, GitHub Releases), observability (OpenTelemetry, health checks, structured logging, PII), and .NET version migration (net8→net9→net10→net11, AOT assessment, nullable migration, upgrade strategies). Spans 19 + 5 topic areas. Do not use for application-layer API or UI implementation patterns.

2026-05-31
dotnet-testing
ソフトウェア品質保証アナリスト・テスター

Defines .NET test strategy and implementation patterns across xUnit v3 (Facts, Theories, fixtures, IAsyncLifetime), integration testing (WebApplicationFactory, Testcontainers), BDD (Reqnroll, lightweight xUnit Given/When/Then), Aspire testing (DistributedApplicationTestingBuilder), snapshot testing (Verify, scrubbing), Playwright E2E browser automation, BenchmarkDotNet microbenchmarks, code coverage (Coverlet), mutation testing (Stryker.NET), UI testing (page objects, selectors), and AOT WASM test compilation. Spans 14 topic areas. Do not use for production API architecture or CI workflow authoring.

2026-05-31
dotnet-tooling
ソフトウェア開発者

Manages .NET SDK installation (dotnet-install, workloads), project bootstrapping (architecture selection, solution structure, domain analysis), project setup (.slnx, Directory.Build.props, CPM), MSBuild authoring, build optimization, performance (Span, ArrayPool, stackalloc), profiling (dotnet-counters, dotnet-trace), Native AOT/trimming, GC tuning, CLI apps (System.CommandLine, Spectre.Console, Terminal.Gui), ILSpy decompilation, VS Code debug config (launch.json, coreclr, remote), C# LSP (csharp-ls, OmniSharp), version detection/upgrade, and code quality cleanup (7-step pipeline: formatting, usings, analyzers, dead code, TODOs, sealed classes, CancellationToken). Spans 36 + 7 topic areas. Do not use for UI implementation or API security design.

2026-05-31
dotnet-ui
ソフトウェア開発者

Builds .NET UI apps across Blazor (Server, WASM, Hybrid, Auto), MAUI (XAML, MVVM, Shell, Native AOT), Uno Platform (MVUX, Extensions, Toolkit), WPF (.NET 8+, Fluent theme), WinUI 3 (Windows App SDK, MSIX, Mica/Acrylic, adaptive layout), and WinForms (high-DPI, dark mode) with JS interop, accessibility (SemanticProperties, ARIA), localization (.resx, RTL), platform bindings (Java.Interop, ObjCRuntime), and framework selection. Spans 20 topic areas. Do not use for backend API design or CI/CD pipelines.

2026-05-31
dotnet-workflow
その他コンピュータ職

Claude Code workflow optimization for .NET development. Covers parallel execution with git worktrees, context discipline (token budget management), plan mode strategy, autonomous loops, verification patterns, and permission setup. Load when setting up Claude Code for a .NET project, running parallel sessions, context is running low, or optimizing productivity. Contains the Learning System reference used by dotnet-learning-agent for correction capture and memory management. Merges workflow-mastery + context-discipline + autonomous-loops + verification-loop.

2026-05-31
using-dotnet
ソフトウェア開発者

Detects .NET intent for any C#, ASP.NET Core, EF Core, Blazor, MAUI, Uno Platform, WPF, WinUI, SignalR, gRPC, xUnit, NuGet, or MSBuild request from prompt keywords and repository signals (.sln, .csproj, global.json, .cs files). First skill to invoke for all .NET work — loads version-specific coding standards and routes to domain skills via [skill:dotnet-advisor] before any planning or implementation. Do not use for clearly non-.NET tasks (Python, JavaScript, Go, Rust, Java).

2026-05-31
dotnet-ai
ソフトウェア開発者

AI/ML integration patterns for .NET. Covers MCP server/client creation and debugging, LLM integration (Semantic Kernel, OpenAI, Azure AI), RAG pipelines, ML.NET model training/inference, and AI technology selection. Load when building AI features in .NET, creating MCP servers, integrating LLMs, or evaluating AI frameworks. Synthesized from dotnet/skills dotnet-ai plugin + dotnet-artisan Semantic Kernel references.

2026-05-28