Skill UI

This guide provides comprehensive best practices for writing robust and high-performance asynchronous code in C#. It covers naming conventions, optimal return types (Task, ValueTask), advanced patterns like the async command pattern, proper exception handling, and crucial performance considerations (e.g., using Task.WhenAll() and avoiding deadlocks with ConfigureAwait(false)). Ideal for developers looking to master modern C# concurrency.

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.

An advanced, AI-powered agent designed to generate comprehensive and workable unit tests for virtually any programming language (e.g., Python, C#, Java, Go, TypeScript). It operates via a sophisticated multi-agent pipeline (Research → Plan → Implement → Build → Test → Fix → Lint). This ensures generated tests are not only written following project conventions but also compile, pass all tests, and effectively improve code coverage.

Generates an AI-ready Architectural Decision Record template with precise sections, coded bullet conventions, front-matter metadata, and sequential storage under /docs/adr to keep decision documentation structured for both machines and people.

Analyzes diverse codebases to auto-detect technology stacks, microservices/monorepo traits, and frontend presence, then documents consistent folder conventions, visualization options, and template guidance to keep large projects organized.

A comprehensive meta-skill template designed for developers to scaffold, structure, and define specialized Agent Skills for AI platforms like GitHub Copilot. It guides users through mandatory requirements, including folder naming conventions, YAML frontmatter best practices (name, description), and structuring the skill body with detailed workflows and documentation sections. Use this when building complex, specialized AI capabilities.

Instrument LLM applications with OpenInference semantic conventions in Phoenix. Covers setup, auto and manual instrumentation, 9 span types, session tracking, production batching and PII masking for Python and TypeScript.

A comprehensive tool that analyzes complex codebases to generate detailed, structured architectural blueprints. It automatically detects and documents the entire technology stack, including primary languages (.NET, Java, Python, etc.), dependencies, versions, licensing, and implementation patterns. The output provides actionable documentation covering coding conventions, usage examples (API, data access), and visual architecture maps, ensuring consistency across guided software development projects.

A comprehensive guide covering best practices for Terraform and OpenTofu. It provides structured advice on module hierarchy, directory organization, naming conventions, and advanced testing strategies (including native testing and policy as code). Use this skill when designing, refactoring, or implementing robust Infrastructure as Code (IaC) pipelines and multi-environment deployments.

This skill generates new UI components that strictly adhere to the StyleSeed design system conventions. It ensures the component is structurally sound, accessible, and uses specified design tokens for colors, spacing, and typography. Use this when creating isolated, reusable UI parts following the defined design system standards.

This skill provides a comprehensive review of UI components and codebases to ensure strict adherence to established design systems, accessibility guidelines (WCAG), and modern front-end best practices. It checks for design token compliance (e.g., semantic colors, scaled spacing), component conventions, WCAG accessibility standards (a11y), mobile responsiveness, and visual coherence across the entire application.

This skill provides comprehensive guidelines for contributing and managing commands within VS Code extensions. It covers crucial best practices, including naming conventions, defining visibility (e.g., Command Palette vs. Side Bar), and structuring commands for optimal user experience. Use this when adding, updating, or understanding the architectural requirements for extension commands.