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This resource is designed to document and guide the implementation of various architectural patterns and best practices when developing backend applications using the NestJS framework. It helps developers structure their code for maximum scalability and maintainability.

This guide outlines how to build new API connectors or providers that strictly adhere to the existing integration architecture and patterns of a target repository. It is designed for adding integrations while maintaining architectural consistency (e.g., layout, configuration schema, authentication model, and testing style), preventing the creation of novel, unsupported patterns.

A comprehensive guide for building production-quality, highly accessible, and responsive user interfaces. Covers advanced topics including component architecture (composition over configuration), robust state management patterns (Context, Redux, Server State), adherence to design systems, and meeting WCAG accessibility standards. Use this skill when developing or refining complex user-facing components that require professional polish and maintainability.

This skill provides comprehensive guidance on idiomatic Go design patterns and best practices for writing production-ready code. It covers functional options, constructor design, graceful shutdown, resource management, dependency injection, and crucial architectural considerations, helping developers write robust and maintainable Go applications.

A comprehensive, type-safe in-memory caching library for Go, designed for advanced system architecture. It supports nine sophisticated eviction algorithms (like W-TinyLFU, LRU, LFU, ARC) to optimize cache hit rates based on specific access patterns. Key features include TTL management, loader chains for read-through caching (with singleflight deduplication), sharding, and Prometheus metrics. Ideal for high-frequency services needing to reduce database load and latency.

Guides Go developers through struct/interface design best practices, covering interface segregation, composition, dependency injection, type assertions, and choosing pointer vs value receivers to keep APIs clear and testable.

This comprehensive guide outlines best practices for writing high-quality, scalable, and maintainable code in .NET/C#. It covers core architectural principles, including SOLID, dependency injection, and design patterns (e.g., Command Handler). Furthermore, it addresses modern challenges such as structured logging, async/await patterns, secure coding, robust testing standards, and advanced AI integration using Semantic Kernel.

A comprehensive code review tool designed for C#/.NET projects. It evaluates the implementation of key design patterns (Command, Factory, Repository) and adherence to SOLID principles. The review covers architecture, best practices (async/await, logging), security, testability, and clean code principles, providing actionable recommendations for improvement.

This skill performs a comprehensive security audit of Model Context Protocol (MCP) server configurations found in .mcp.json files. It is designed to identify critical vulnerabilities, including hardcoded secrets (API keys, tokens), dangerous shell injection patterns, unpinned dependencies, and compliance issues regarding approved server lists. Use this skill during security reviews, project onboarding, or pre-commit checks to ensure secure deployment.

This library provides a complete and verified set of grep command patterns designed for auditing existing React codebases. It is essential for developers preparing for major React upgrades, specifically migration audits for React 18.3.1 or React 19. It detects deprecated APIs, removed features, unsafe lifecycle methods, batching vulnerabilities, and dependency conflicts, ensuring a smooth transition.

Catalogs 42 cloud-agnostic design patterns that architects can apply when designing, reviewing, or operating distributed systems to boost reliability, performance, messaging, security, and deployment outcomes.

Guides architects through Dataverse SDK use cases by analyzing requirements, designing data models, selecting integration patterns, and producing production-ready Python code with best practices.