Csharp Dotnet Development logo

Csharp Dotnet Development

Community
peterbamuhigire
csharp-dotnet-development

Use when building, reviewing, modernising, or debugging C# and .NET applications, including ASP.NET Core, EF Core, background services, concurrency, .NET MAUI, testing, packaging, and AI integration.

Overview

Publisherpeterbamuhigire
Repositorychwezi-dev-engine
Skill namecsharp-dotnet-development
Stars
29
Forks
6
Bundled files
8
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 8 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by peterbamuhigire on GitHub. Read the source before you install it.

Installation

Install the Csharp Dotnet Development AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/peterbamuhigire/chwezi-dev-engine/tree/main/skills/languages/csharp-dotnet-development
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/peterbamuhigire/chwezi-dev-engine.git /tmp/chwezi-dev-engine
mkdir -p .claude/skills
cp -r /tmp/chwezi-dev-engine/skills/languages/csharp-dotnet-development .claude/skills/csharp-dotnet-development
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Csharp Dotnet Development in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Csharp Dotnet Development on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Csharp Dotnet Development is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

C# And .NET Development

Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, +256 784 464178.

Use When

  • Building or reviewing C#/.NET code, services, APIs, workers, desktop/mobile apps, or libraries.
  • Choosing .NET target frameworks, SDK versions, project layout, dependency boundaries, or deployment shape.
  • Implementing ASP.NET Core endpoints, EF Core repositories, background jobs, async/parallel code, MAUI UI, Azure services, or .NET AI features.
  • Modernizing older .NET Framework, Web Forms, WPF, WinForms, or early .NET Core code to current .NET.

Do Not Use When

  • The work is only Avalonia UI; use avalonia-desktop-development for AXAML, Avalonia MVVM, styling, and packaging.
  • The work is only generic architecture, data modelling, security, or CI/CD with no .NET-specific decisions; use the specialist skill first and return here for implementation details.

Required Inputs

ArtefactRequired?Purpose
Target framework, SDK, hosting model, and deployment targetyesFix runtime and compatibility boundaries
Solution, failures, logs, and testsyesGround work in repository evidence
Integrations, security model, and scale assumptionsconditionalShape operational decisions

Workflow

  1. Identify the application shape: library, CLI, ASP.NET Core, worker, MAUI, desktop, Azure function, microservice, or AI-enabled service.
  2. Verify the target framework and SDK. Prefer current LTS for production unless the project is explicitly pinned or compatibility requires otherwise.
  3. Inspect project boundaries. Keep domain logic outside controllers, pages, XAML code-behind, EF DbContext, hosted services, and framework glue.
  4. Apply C# idioms from references/csharp-language-and-runtime.md before adding abstractions.
  5. Load only the relevant deep reference:
    • APIs/services: references/aspnet-core-and-services.md
    • EF Core/data: references/ef-core-data-access.md
    • MAUI UI: references/dotnet-maui-cross-platform-ui.md
    • Async/parallel work: references/concurrency-and-parallelism.md
    • Testing/release: references/testing-packaging-and-operations.md
    • AI integration: references/ai-in-dotnet.md
    • Source synthesis: references/source-study-notes.md
  6. Implement or review with security, cancellation, structured logging, observability, performance, and testability as first-class concerns.
  7. Validate with dotnet restore, dotnet build, dotnet test, analyzers, formatters, and a realistic runtime smoke test when a project is available.

Quality Standards

  • Treat nullable reference types, analyzers, warnings, and source-generated validation as quality gates, not optional polish.
  • Use dependency injection and options binding deliberately; keep service lifetimes correct and avoid service locator patterns.
  • Make all I/O async and cancellation-aware. Do not block on Task.Result, .Wait(), or .GetAwaiter().GetResult() in app code.
  • Validate every boundary: HTTP, CLI args, config, queues, files, webhooks, external API responses, and AI/tool outputs.
  • Prefer records/immutable value objects for data transfer and domain values; use classes for behaviour and identity.
  • Keep EF Core queries explicit, measured, and projection-focused; avoid lazy-loading surprises on request paths.
  • Add logging scopes, correlation IDs, health checks, metrics, and actionable errors before calling a service production-ready.
  • Use platform-specific UI only behind thin adapters; keep MAUI view models and domain services testable without device APIs.

Anti-Patterns

  • Fat controllers, fat pages, fat hosted services, or XAML code-behind containing domain rules.
  • async void outside UI event handlers, fire-and-forget tasks without supervised queues, or ignored CancellationToken.
  • Returning EF entities directly from public APIs when contracts, privacy, or versioning matter.
  • Global static state for clocks, configuration, database access, HTTP clients, or current user context.
  • Swallowing exceptions, logging without context, or exposing raw exception details to users.
  • Hand-written cryptography, custom authentication/session systems, or direct secret reads from source files.
  • Treating .NET Framework, .NET, .NET Core, ASP.NET, ASP.NET Core, MAUI, WPF, and Avalonia as interchangeable.

Outputs

  • Code changes, review findings, architecture decisions, migration plans, test strategy, release checklist, or implementation guidance.
  • Explicit assumptions about target framework, platform support, data stores, threading, deployment, and compatibility.
  • Validation evidence: commands run, tests added or skipped with reason, runtime smoke checks, and residual risk.

Evidence Produced

CategoryArtifactFormatExample
Correctness.NET verification logMarkdown or terminal summary with restore/build/test/analyzer/runtime smoke resultsdocs/dotnet/verification.md
CorrectnessBoundary mapMarkdown table of projects, responsibilities, dependencies, and forbidden referencesdocs/dotnet/project-boundaries.md
OperabilityRelease readiness checklistMarkdown checklist for config, health, telemetry, packaging, rollback, and ownerdocs/dotnet/release-readiness.md

References

  • references/csharp-language-and-runtime.md: load for language idioms, C# 12/13/14 features, project files, nullability, records, spans, exceptions, and runtime choices.
  • references/aspnet-core-and-services.md: load for APIs, minimal APIs, controllers, middleware, auth, OpenAPI, Blazor, SignalR, hosted services, and Azure functions.
  • references/ef-core-data-access.md: load for EF Core modelling, migrations, query performance, transactions, concurrency, JSON/vector features, and repository boundaries.
  • references/dotnet-maui-cross-platform-ui.md: load for .NET MAUI app structure, XAML, MVVM, platform services, device features, and packaging.
  • references/concurrency-and-parallelism.md: load for async workflows, channels, task parallelism, dataflow, cancellation, locking, and CPU-bound work.
  • references/testing-packaging-and-operations.md: load for test strategy, analyzers, CI, publishing, containers, NativeAOT, trimming, health checks, and observability.
  • references/ai-in-dotnet.md: load for Semantic Kernel, Microsoft.Extensions.AI, embeddings, RAG, tool calling, safety, and telemetry.
  • references/source-study-notes.md: load when you need the source-book map and synthesis boundaries used to prepare this skill.

Project Defaults

Prefer this baseline unless the target project already has stronger conventions:

  • Directory.Build.props with nullable enabled, implicit usings enabled, latest stable language version only when the SDK supports it, warnings treated seriously, and analyzers on.
  • One solution with explicit projects: Domain, Application, Infrastructure, Api/Worker/Maui, and Tests for non-trivial systems.
  • External dependencies behind interfaces or typed clients; contracts versioned separately from EF entities and UI models.
  • IOptions<T> with validated configuration; secrets from environment, user secrets for local development, or managed secret stores.
  • TimeProvider, ILogger<T>, IHttpClientFactory, IHostedService/BackgroundService, and CancellationToken instead of ad hoc infrastructure.
  • dotnet format, analyzers, unit tests, integration tests, and smoke tests in CI.

Review Checklist

  • Does each project have a clear reason to exist, and are dependencies one-way?
  • Are public contracts stable, documented, validated, and decoupled from persistence?
  • Are async calls cancellation-aware and free from sync-over-async?
  • Are secrets, auth, authorization, CORS, cookies, tokens, and data access scoped correctly?
  • Are database queries projected, indexed, bounded, and observable?
  • Are tests proving domain rules, API contracts, persistence behaviour, and critical failure paths?
  • Can production operators diagnose startup, dependency failure, queue backlog, request latency, and release version quickly?

Decision Rules

ConditionAction
Supported target framework existsImprove in place and preserve public contracts
Upgrade crosses compatibility boundariesStage migration with rollback points
Failure is runtime-specificReproduce against the pinned SDK first

Capability Contract

Read and search are required. Editing and execution require task authorisation; network access is optional for documentation and package verification.

Degraded Mode

If execution is unavailable, return a code review and exact verification commands without claiming build or test success.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Csharp Dotnet Development AI skill do?

Use when building, reviewing, modernising, or debugging C# and .NET applications, including ASP.NET Core, EF Core, background services, concurrency, .NET MAUI, testing, packaging, and AI integration.

Why use Csharp Dotnet Development on TypingMind?

Because you install it once and use it with any model. Csharp Dotnet Development is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Csharp Dotnet Development in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/peterbamuhigire/chwezi-dev-engine/tree/main/skills/languages/csharp-dotnet-development. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Csharp Dotnet Development?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Csharp Dotnet Development?

As many as you like. As long as a model supports skills, you can use Csharp Dotnet Development with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Csharp Dotnet Development AI skill free?

Yes. It is published on GitHub by peterbamuhigire under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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