dotnet-best-practices

.NET/C# Best Practices

Your task is to ensure .NET/C# code in the selected code block meets the best practices specific to this solution/project. If no code selection is active when this skill is invoked, stop immediately and ask the user to select the specific class, interface, or method they want reviewed before proceeding. This includes:

Before Applying These Practices

Before applying any recommendation, inspect the project's .csproj and solution files to determine:

• The actual <TargetFramework> or <TargetFrameworks> in use — apply language and runtime features only available in that version.
• The test framework referenced in the test project (MSTest, xUnit, NUnit) — use the framework already present, not the one listed below.
• The mocking library already in use (Moq, NSubstitute, FakeItEasy) — do not introduce a different library.
• The existing namespace conventions — prefer the pattern already established in the codebase.

Where any recommendation in this guide conflicts with an established project pattern, prefer the existing pattern and flag the discrepancy as a comment in your response rather than silently overriding it.

Documentation & Structure

• Create comprehensive XML documentation comments for all public classes, interfaces, methods, and properties
• Include parameter descriptions and return value descriptions in XML comments
• Follow the established namespace structure: {Core|Console|App|Service}.{Feature}

Design Patterns & Architecture

• Use primary constructor syntax for dependency injection (e.g., public class MyClass(IDependency dependency))
• Implement the Command Handler pattern with generic base classes (e.g., CommandHandler<TOptions>)
• Use interface segregation with clear naming conventions (prefix interfaces with 'I')
• Follow the Factory pattern for complex object creation.

Dependency Injection & Services

• Use constructor dependency injection with null checks via ArgumentNullException
• Register services with appropriate lifetimes (Singleton, Scoped, Transient)
• Use Microsoft.Extensions.DependencyInjection patterns
• Implement service interfaces for testability

Resource Management & Localization

• Use ResourceManager for localized messages and error strings
• Separate LogMessages and ErrorMessages resource files
• Access resources via _resourceManager.GetString("MessageKey")

Async/Await Patterns

• Use async/await for all I/O operations and long-running tasks
• Return Task or Task<T> from async methods
• Use ConfigureAwait(false) where appropriate
• Handle async exceptions properly

Testing Standards

• Use MSTest framework with FluentAssertions for assertions
• Follow AAA pattern (Arrange, Act, Assert)
• Use Moq for mocking dependencies
• Test both success and failure scenarios
• Include null parameter validation tests

Configuration & Settings

• Use strongly-typed configuration classes with data annotations
• Implement validation attributes (Required, NotEmptyOrWhitespace)
• Use IConfiguration binding for settings
• Support appsettings.json configuration files
• Never store API keys, connection strings with credentials, or any secret values in appsettings.json or appsettings.{Environment}.json. Use dotnet user-secrets for local development, environment variables for CI/CD pipelines, and a managed secret store (Azure Key Vault, AWS Secrets Manager, HashiCorp Vault) for production deployments. Verify that appsettings.json contains only non-secret configuration before committing.

Semantic Kernel & AI Integration

• Use Microsoft.SemanticKernel for AI operations
• Implement proper kernel configuration and service registration
• Handle AI model settings (ChatCompletion, Embedding, etc.)
• Use structured output patterns for reliable AI responses

Error Handling & Logging

• Use structured logging with Microsoft.Extensions.Logging
• Include scoped logging with meaningful context
• Throw specific exceptions with descriptive messages
• Use try-catch blocks for expected failure scenarios

Performance & Security

• Use C# 12+ features and .NET 8 optimizations where applicable
• Implement proper input validation and sanitization
• Use parameterized queries for database operations
• Follow secure coding practices for AI/ML operations

Code Quality

• Ensure SOLID principles compliance
• Avoid code duplication through base classes and utilities
• Use meaningful names that reflect domain concepts
• Keep methods focused and cohesive
• Implement proper disposal patterns for resources