8229a03986
- Introduced a prompt for creating standardized documentation for object-oriented components, adhering to industry best practices and architectural standards. - Added a prompt for generating new specification files optimized for Generative AI consumption, including best practices for AI-ready specifications. - Created a prompt for reviewing .NET/C# code against best practices, focusing on documentation, design patterns, dependency injection, and error handling. - Developed a prompt for reviewing design pattern implementations in .NET/C# code, providing a checklist and improvement focus areas. - Implemented a prompt for updating Azure Verified Modules in Bicep files to the latest versions, including a breaking change policy. - Added a prompt for updating existing implementation plans with new requirements or updates to existing code. - Created a prompt for updating the llms.txt file to reflect changes in documentation or specifications. - Developed a prompt for updating markdown file sections with an index/table of files from specified folders. - Introduced a prompt for updating existing object-oriented component documentation based on current implementation. - Added a prompt for updating existing specifications based on new requirements or code updates.
84 lines
3.1 KiB
Markdown
84 lines
3.1 KiB
Markdown
---
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mode: 'edit'
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---
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# .NET/C# Best Practices
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Your task is to ensure .NET/C# code in ${selection} meets the best practices specific to the this soltion/project. This includes:
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## Documentation & Structure
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- Create comprehensive XML documentation comments for all public classes, interfaces, methods, and properties
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- Include parameter descriptions and return value descriptions in XML comments
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- Follow the established namespace structure: {Core|Console|App|Service}.{Feature}
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## Design Patterns & Architecture
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- Use primary constructor syntax for dependency injection (e.g., `public class MyClass(IDependency dependency)`)
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- Implement the Command Handler pattern with generic base classes (e.g., `CommandHandler<TOptions>`)
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- Use interface segregation with clear naming conventions (prefix interfaces with 'I')
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- Follow the Factory pattern for complex object creation.
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## Dependency Injection & Services
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- Use constructor dependency injection with null checks via ArgumentNullException
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- Register services with appropriate lifetimes (Singleton, Scoped, Transient)
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- Use Microsoft.Extensions.DependencyInjection patterns
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- Implement service interfaces for testability
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## Resource Management & Localization
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- Use ResourceManager for localized messages and error strings
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- Separate LogMessages and ErrorMessages resource files
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- Access resources via `_resourceManager.GetString("MessageKey")`
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## Async/Await Patterns
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- Use async/await for all I/O operations and long-running tasks
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- Return Task or Task<T> from async methods
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- Use ConfigureAwait(false) where appropriate
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- Handle async exceptions properly
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## Testing Standards
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- Use MSTest framework with FluentAssertions for assertions
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- Follow AAA pattern (Arrange, Act, Assert)
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- Use Moq for mocking dependencies
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- Test both success and failure scenarios
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- Include null parameter validation tests
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## Configuration & Settings
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- Use strongly-typed configuration classes with data annotations
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- Implement validation attributes (Required, NotEmptyOrWhitespace)
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- Use IConfiguration binding for settings
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- Support appsettings.json configuration files
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## Semantic Kernel & AI Integration
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- Use Microsoft.SemanticKernel for AI operations
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- Implement proper kernel configuration and service registration
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- Handle AI model settings (ChatCompletion, Embedding, etc.)
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- Use structured output patterns for reliable AI responses
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## Error Handling & Logging
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- Use structured logging with Microsoft.Extensions.Logging
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- Include scoped logging with meaningful context
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- Throw specific exceptions with descriptive messages
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- Use try-catch blocks for expected failure scenarios
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## Performance & Security
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- Use C# 12+ features and .NET 8 optimizations where applicable
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- Implement proper input validation and sanitization
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- Use parameterized queries for database operations
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- Follow secure coding practices for AI/ML operations
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## Code Quality
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- Ensure SOLID principles compliance
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- Avoid code duplication through base classes and utilities
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- Use meaningful names that reflect domain concepts
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- Keep methods focused and cohesive
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- Implement proper disposal patterns for resources
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