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Review .NET/C# code: ASP.NET Core, EF Core, Minimal APIs, Azure patterns and performance
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Based on SOC occupation classification
| name | csharp-dotnet-review |
| description | Review .NET/C# code: ASP.NET Core, EF Core, Minimal APIs, Azure patterns and performance |
| triggers | {"frameworks":["dotnet","aspnet","efcore","blazor"],"file-patterns":["**/*.cs","**/*.csproj"]} |
| preferred-model | sonnet |
| min-confidence | 0.4 |
| depends-on | [] |
| category | code-quality |
| estimated-tokens | 5000 |
| tags | ["csharp","dotnet","aspnet"] |
new Service())IOptions<T>)async/await used correctly (no .Result or .Wait())CancellationToken propagated through async chainsIDisposable implemented for unmanaged resourcesnull handled with nullable reference types (?, ??, ?.)sealed classes where inheritance not neededreadonly for immutable fields.Include() or .AsSplitQuery())AsNoTracking() for read-only queries[Authorize] on all protected endpointsIMemoryCache or IDistributedCache for hot dataSystem.Text.Json configured (not Newtonsoft unless needed)IHostedService / Hangfire## .NET Review: [Component]
**Health Score**: X/10
### Critical Issues
- [Issue with fix]
### Improvements
- [Suggestion with example]
### Architecture Notes
- [Pattern observations]
**Architecture Decision Record (ADR) — Philosophy-First**: Creates well-structured ADRs using a 3-phase process: (1) Define the architectural philosophy and identity of the system, (2) Explore options through the lens of that philosophy, (3) Adversarial review by a devil's advocate agent. Use this skill whenever the user wants to document a technical decision, create an ADR, record why a technology or approach was chosen, compare architectural alternatives, or mentions 'ADR', 'architecture decision', 'decision record', 'technical decision', 'why did we choose', or 'document this decision'. Also trigger when the user is evaluating trade-offs between technical approaches and wants to formalize the reasoning.
**AI/ML Engineering Review**: Reviews AI/ML systems for production readiness — model serving, MLOps pipelines, LLM integration patterns, prompt engineering, evaluation frameworks, and responsible AI. Covers model deployment, feature stores, experiment tracking, monitoring/drift detection, and AI safety. Use when the user mentions ML, AI, machine learning, model, LLM, GPT, Claude, embeddings, RAG, fine-tuning, MLOps, model serving, feature store, or any AI/ML infrastructure.
**API Documentation & Design (OpenAPI/Swagger)**: Helps write and review API documentation, generate OpenAPI/Swagger specs, design RESTful APIs, and document GraphQL schemas. Use whenever the user mentions 'API docs', 'Swagger', 'OpenAPI', 'API specification', 'API design', 'REST API', 'endpoint documentation', 'API contract', 'API versioning', 'GraphQL schema', 'API reference', or asks to document their API, generate a Swagger spec, design API endpoints, or review API contracts for consistency.
API Gateway design patterns including rate limiting, authentication, versioning, BFF and circuit breaking
ARB process design, RFC governance, decision log and technical review workflows
**Backend Code Review (Node.js, Java, Microservices)**: Expert review of backend code focusing on Node.js, Java, Clean Architecture, SOLID principles, microservices patterns, SQL/database design, messaging (Kafka, SQS, SNS), and payment flows. Use whenever the user wants a review of backend code, API design, service architecture, database queries, or mentions Node, Java, Spring, NestJS, Express, microservices, REST API, gRPC, or asks to review server-side code. Also trigger for database schema reviews, query optimization, and message queue patterns.