Skill UI

This guide outlines the architecture and implementation steps for building a robust, production-ready design system. It covers core elements like design tokens, reusable components, and layout patterns. Ideal for teams needing to maintain design consistency, ensure accessibility, and synchronize design (Figma) with implementation (Storybook).

A comprehensive multi-agent feedback loop inspired by Generative Adversarial Networks (GANs), designed to build complex, production-ready applications. It separates generation from evaluation, using a 'ruthlessly strict' evaluator (QA) to drive continuous iteration. This process ensures high design quality, robust functionality, and originality, overcoming the limitations of single-agent self-critique. Ideal for full-stack feature development and complex product specification implementation.

Craft production-ready frontend pages, dashboards, or components with a clear visual point of view; focus on typography, color, motion, and layout so the experience feels intentional instead of generic.

Guides building production-ready user interfaces with thoughtful component architecture, consistent spacing, typography, color, and accessibility standards while avoiding generic AI aesthetics and over-configured patterns.

A comprehensive guide to creating production-ready, idiomatic Go applications. Covers critical patterns such as functional options, robust error handling, graceful shutdown, resource management, and architectural best practices. Ideal for refactoring existing Go code, choosing between architectural patterns, or designing scalable APIs and constructors.

A comprehensive guide to implementing robust, idiomatic error handling in Go. It covers best practices for error creation, context wrapping using `%w`, type-safe inspection with `errors.Is`/`errors.As`, adhering to the single handling rule, and utilizing structured logging with `slog`. Essential for building scalable, debuggable, and production-ready Go applications.

This skill acts as a Go ecosystem expert, providing recommendations for production-ready libraries and frameworks. It guides developers through selecting the simplest, most performant, and idiomatic Go solution, prioritizing the standard library first and only suggesting external dependencies when they provide clear, necessary value.

This skill guides the generation of a complete, production-ready Model Context Protocol (MCP) server using C# and .NET. It covers advanced architectural patterns, including Host builder setup, proper dependency injection, structured logging, and implementing robust functional tools. Ideal for developers building sophisticated AI agents or backend services that require standardized communication and tool invocation capabilities.

A comprehensive guide for resolving complex timezone issues in .NET and C# applications. It provides production-safe patterns for handling location lookups, converting between Windows and IANA timezone IDs, managing Daylight Saving Time (DST), and ensuring cross-platform compatibility. Includes ready-to-use code snippets for TimeZoneInfo, NodaTime, and DateTimeOffset.

This utility automates the creation of a complete, production-ready Model Context Protocol (MCP) server project using Java. It generates full source code templates, including configuration, tool handling, resource definitions, and prompt logic. The generator provides robust setup for both Maven and Gradle, ensuring the resulting backend service is structured, scalable, and ready for deployment with support for reactive streams and optional Spring Boot integration.

This generator provides a comprehensive, production-ready template for building a Model Context Protocol (MCP) server using Kotlin. It sets up the full Gradle structure, integrates essential dependencies (MCP SDK, Ktor, Coroutines), and includes robust placeholders for custom tools, error handling, and testing, accelerating the development of context-aware backend services.

This tool automates the entire process of creating a complete, production-ready application structure solely from an OpenAPI (Swagger) specification. It analyzes the spec to design the application architecture, generating all necessary layers including controllers, service logic, data models (DTOs), and repositories, adhering to best practices, framework conventions, and separation of concerns. The output includes unit tests, configuration files, and setup instructions, providing a robust, ready-to-use codebase.