Skill UI

Analyzes IaC files and live Azure resources to validate SKUs, usage, and costs, then generates GitHub issues (one per recommendation plus an EPIC) so teams can track implementation of prioritized savings.

Guides teams through creating a complete GitHub Copilot configuration by outlining required instructions, reusable skills, and agent/workflow files tailored to the chosen tech stack and workflow preferences.

A comprehensive template for planning large-scale, multi-file refactoring tasks. It enforces a systematic approach by requiring developers to map out the current state, target state, affected files, and execution phases (Types, Implementation, Tests). Crucially, it mandates detailed verification and a robust rollback plan to ensure code stability during major changes.

Scans repository context and chat history to recommend awesome-copilot Custom Agent files that are missing or outdated locally, outlines relevance/rationale, and waits for user approval before installing or updating.

Suggests relevant GitHub Copilot instructions by comparing awesome-copilot guidance with the local repo context, identifying duplicates, and flagging outdated files.

A comprehensive toolkit designed to automate virtually all interactions with the Twitter/X platform via Rube MCP. It supports creating, managing, and deleting posts; performing advanced searches across recent or full archives; looking up user profiles by ID or username; managing bookmarks and lists; and interacting with content (liking/unliking). Essential for integrating social media management into automated workflows.

Automates updating Azure Verified Modules referenced in Bicep files to their latest released versions, guiding scan, version comparison, documentation checks, and validation workflows while limiting reports to non-breaking changes.

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.

This tool reviews user interface (UI) code files to ensure compliance with established Web Interface Guidelines and best practices. Use it when auditing design, checking accessibility, or performing general code reviews to meet industry standards and improve overall product quality. It fetches the latest rules and outputs detailed findings.

This structured prompt guides an AI model to proactively analyze a user's question and systematically list all necessary files for accurate answering. It forces the AI to categorize required context into 'Must See' (critical), 'Should See' (helpful), 'Already Have,' and 'Uncertainties.' Ideal for complex coding tasks, debugging, or feature implementation that spans multiple codebase files.

This specialized skill automates the process of converting visual designs (from Stitch) into high-quality, modular React and Vite components. It adheres to modern frontend best practices, enforcing logic isolation via custom hooks, data decoupling using mock data files, and ensuring type safety with TypeScript interfaces. The workflow includes automated asset fetching, style mapping (Tailwind), and rigorous validation cycles to produce production-ready, maintainable code.

This skill generates comprehensive and structured requirements documents for a new specification. It systematically aggregates context from project files, business rules, and technical constraints, ensuring all generated requirements focus on user-observable behavior (WHAT) rather than implementation details (HOW). It follows established standards like EARS format and includes a rigorous review gate to ensure scope clarity and completeness.