Skill UI

This skill transforms a raw Python script into a polished, beginner-friendly project package. It automatically refactors the code to adhere to PEP 8 standards, adds comprehensive, instructional comments explaining the 'why' behind the logic, and generates a complete, professional `README.md` file. The resulting project is immediately ready for use in educational settings, portfolios, or for onboarding new developers.

Generates a copilot-instructions blueprint tailored to each repository by analyzing existing code patterns, versions, and architectural standards so GitHub Copilot stays aligned with project guidelines.

Master the art of developing robust and scalable Python applications that interface with Microsoft Dataverse. This skill covers implementing enterprise-grade best practices, including comprehensive error handling using DataverseError hierarchy, advanced retry logic with exponential backoff, and optimizing API calls using OData standards (filtering, selecting, and expanding). It ensures code is clean, maintainable, and production-ready.

This tool acts as an expert prompt engineer, guiding users through a systematic discovery process to create highly structured, production-ready prompts for AI assistants like GitHub Copilot. It covers defining persona, specifying complex tasks, managing context variables, integrating tools, and enforcing strict output standards, ensuring the resulting prompt is comprehensive and actionable for advanced AI agents.

This guide serves as a comprehensive reference for source-file migration patterns required when upgrading to React 19. It details critical API changes, such as updating `ReactDOM.render` to `createRoot().render()`, modernizing ref handling, and adjusting legacy context usage. It is essential for developers migrating large, existing React applications to ensure compatibility with the latest React standards.

Analyzes Copilot guidance files and repository docs to craft structured README.md content, detailing the project purpose, architecture, technology stack, workflows, coding standards, testing approach, and contributing advice for fellow developers.

Acts as a senior software engineer to meticulously review and refactor entire codebases. This skill ensures that the code is clean, highly maintainable, and strictly adheres to defined coding standards and best practices specified across multiple project instruction files. It guarantees the integrity of the existing file structure and ensures all unit tests remain passing after modifications.

Defines Salesforce Lightning Web Component, Aura, and Visualforce quality/security standards covering SLDS 2 styling, accessibility (WCAG 2.1 AA), data access patterns, component communication rules, XSS/CSRF protections, view state management, and Jest testing requirements for guided UI development and reviews.

A comprehensive, five-stage methodology for reviewing code quality. This process systematically checks for requirements compliance, logical correctness, code maintainability, test coverage, and critical security/performance vulnerabilities. It provides actionable feedback categorized by severity (Blocker, Major, Minor, Nit), ensuring high standards for all merged code.

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 guide provides a comprehensive template and best practices for updating technical specification files. It ensures that the resulting documentation is structured, unambiguous, and highly optimized for consumption by Generative AI models. Use this process when refining system requirements, updating interfaces, or documenting architectural changes to maintain machine-readable standards.