Skill UI

This guide provides a comprehensive roadmap for migrating legacy AngularJS (1.x) applications to modern Angular (2+). It covers essential techniques like component and directive conversion, dependency injection updates, and routing migration. Learn advanced strategies for building hybrid apps and executing safe, incremental upgrades for large-scale enterprise applications.

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.

This template provides production-ready FastAPI project skeletons, incorporating advanced features such as async patterns, dependency injection, middleware, and robust error handling. It is essential for developers starting new high-performance REST APIs, microservices, or complex backend systems in Python, ensuring adherence to best architectural practices.

Build and configure Laravel 10+ apps using Eloquent models, Sanctum auth, Horizon queues, Livewire interfaces, and REST APIs while ensuring PSR-12, dependency injection, eager loading, and Pest/PHPUnit coverage above 85%.

Expert skill for building robust, enterprise-grade PHP applications using modern features (PHP 8.3+), major frameworks (Laravel, Symfony), and strict architectural patterns. Specializes in implementing typed DTOs, value objects, dependency injection, and ensuring high test coverage for secure REST/GraphQL APIs.

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).

A comprehensive security scanner designed to audit AI agent skills before deployment. It analyzes source code (Python, Bash, JS), detects critical vulnerabilities like command injection, network exfiltration, and unsafe deserialization. Furthermore, it scans markdown files for prompt injection attacks, and assesses dependency supply chain risks, providing a clear PASS/WARN/FAIL verdict and remediation guidance.

A guide to building robust, type-safe, and highly testable backend services using functional programming patterns in Node.js or Deno. This pattern utilizes fp-ts and ReaderTaskEither to manage dependency injection, compose complex business logic, and handle typed errors gracefully, promoting clean service layer architecture.

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.

A robust security auditing tool designed to scan CI/CD pipeline configurations, such as GitHub Actions YAML, for critical supply chain vulnerabilities. It automatically detects issues like unpinned dependencies, script injection risks, secrets exposure, and dependency confusion, helping teams harden their build systems and maintain secure software development lifecycles.

A comprehensive guide to building production-grade Laravel applications. It details architectural patterns for achieving scalability and maintainability, covering service/action separation, advanced Eloquent usage (casts, scopes), dependency injection via service containers, and robust API routing patterns.

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.