Skill UI

This guide outlines architectural patterns and best practices for building scalable, production-ready APIs with FastAPI. It covers proper project structure, separating concerns using routers, services, and CRUD layers, implementing dependency injection for database sessions and authentication, defining Pydantic schemas for request/response, handling asynchronous operations, and managing custom error responses.

A comprehensive set of architectural patterns for building robust and maintainable HTTP servers using Kotlin Coroutines and the Ktor framework. It covers essential topics including REST routing DSL, plugin configuration (Authentication, CORS, StatusPages), Dependency Injection (Koin), serialization, WebSockets, and integration testing using testApplication.

This comprehensive guide demonstrates how to achieve highly testable Swift code using protocol-based dependency injection. By abstracting external concerns—such as file systems, networking, and external APIs—into small, focused protocols, developers can write deterministic unit tests that mock side effects and error paths, ensuring robust architecture across different environments (App, Test, Preview).

This tool simulates a senior application security engineer, performing a comprehensive, multi-phase audit. It checks for exposed secrets, prompt injection vectors, dependency vulnerabilities (supporting multiple languages), and critical configuration misconfigurations. It provides a scored, prioritized remediation report, ensuring a robust security posture before deployment.

Skill for adding, modifying, or reviewing CLI commands in .NET projects using System.CommandLine v2.x. Covers command architecture, base classes, options, arguments, handlers, subcommands, registration, user confirmation, and dependency injection patterns.

This skill detects supply chain compromise risks in AI agents. It identifies vulnerabilities related to compromised dependencies, malicious plugins, tools, models, and untrusted update sources. It is used when agents dynamically load external resources or pull remote configurations. The skill probes for untrusted loading, dependency integrity, and malicious artifact injection to ensure agent security.

Master AngularJS to Angular migration with strategies for hybrid apps, component conversion, dependency injection changes, and routing migration. Includes practical code examples for ngUpgrade setup, service migration, and best practices for incremental or big-bang transitions.

Detailed guidelines for production-grade backend development using Node.js, Express, TypeScript, and microservices. Covers layered architecture, error handling, dependency injection, validation, and observability with Sentry, Prisma, and Zod.

This skill guides the development of production-grade APIs, MCP servers, and enterprise backends using modern C#/.NET best practices. It covers essential architectural components such as dependency injection (DI), advanced caching strategies (e.g., Redis), data access optimization with EF Core/Dapper, and implementing resilience patterns, ensuring robust and scalable software design.

Build production-ready FastAPI projects with async patterns, dependency injection, middleware, and error handling. Ideal for new Python REST APIs, microservices, and high-performance web backends.

This skill provides functional programming patterns for building type-safe and testable backend services using fp-ts in Node.js or Deno environments. It covers dependency injection, service composition, and handling typed backend errors with ReaderTaskEither. Ideal for developers implementing functional architecture principles in modern JavaScript runtimes to ensure robust and maintainable server-side applications.

PydanticAI is a robust Python framework that integrates Pydantic's type-checking and validation capabilities into Large Language Models (LLMs). It allows developers to build production-ready AI agents that guarantee structured, validated outputs, moving beyond simple raw strings. Key features include supported tool calling, dependency injection for testability, and multi-model compatibility across major providers like OpenAI, Anthropic, and Gemini. Use this when reliability and correct data structure are paramount.