Skill UI

Expert skill for building Model Context Protocol (MCP) servers and tools from scratch. Covers the full development lifecycle—from defining tool schemas and resources to implementation, testing, and publishing to registries. Supports robust patterns using TypeScript and Python for integrating custom capabilities into AI agents.

Generates comprehensive, long-form cheat sheets for advanced Machine Learning and Large Language Model (LLM) topics. These tutorials are highly structured, including mathematical derivations, from-scratch PyTorch implementations, detailed comparison tables, and 25 categorized high-frequency interview questions (L1, L2, L3). Ideal for deep academic study and technical interview preparation.

This skill conducts a comprehensive audit to identify and prioritize automation opportunities within a user's existing workflow. It guides users through a structured intake (covering roles, repetitive tasks, and pain points) and systematically scans connected tools (like Gmail, Slack, Calendar, and CRMs) to surface actionable, ranked quick wins, minimizing implementation effort and maximizing impact.

Invariant-Guard enforces a correctness-first approach to algorithm design. Instead of writing code and hoping it works, this skill mandates defining the function contract, loop invariants, termination arguments, and all potential edge cases *before* implementation. It is essential for complex algorithms where subtle bugs (like off-by-one errors or missed post-conditions) are common, ensuring mathematically rigorous and verifiable code.

This skill enforces an algorithm-first discipline, requiring the user to state the time and space complexity (Big-O), the intended data structure, and the core algorithm family before writing any complex code (loops, queries, recursion). It serves as a critical gatekeeper to prevent common performance pitfalls like O(n²) complexity, N+1 database queries, and brute-force implementation errors, significantly improving code robustness and efficiency.

This skill generates robust JSON-LD structured data markup for various web components, including blogs, FAQs, tools, and SaaS pages. By implementing standard Schema.org types (WebSite, Article, FAQPage, etc.), you can significantly improve your SEO, boost click-through rates (CTR), and provide clear context to both search engines and AI systems. Includes implementation guidance for modern frameworks like Next.js.

This skill helps developers fix social sharing previews, ensuring that URLs render as rich, consistent cards across major platforms like Facebook, LinkedIn, X/Twitter, and WhatsApp. It provides standardized implementation for Open Graph (OG) tags and Twitter cards, crucial for improving SEO and discoverability.

This guide provides best practices for building flexible, maintainable, and scalable React components. It emphasizes using composition patterns and compound components to avoid 'boolean prop proliferation,' while also detailing advanced concepts in component architecture, state management, and modern implementation techniques (including React 19 APIs). Ideal for refactoring complex systems or designing reusable component libraries.

Provides a comprehensive, step-by-step workflow for developers to integrate new Azure Backup commands into the Azure Backup MCP toolset. It covers the entire development lifecycle, including option definition, service implementation, command logic, command registration, input validation, writing unit tests, and executing live/recorded tests, ensuring high quality and adherence to best practices before PR submission.

This skill automates the transformation of high-level product specifications (PRDs) or feature specs stored in Notion into an actionable development lifecycle. It systematically parses requirements, generates structured implementation plans, breaks down complex needs into manageable, linked tasks, and facilitates continuous progress tracking, ensuring all development artifacts remain synchronized with the original spec.

This Main Agent acts as a central orchestrator for a specialized squad of AI agents, managing complex software development lifecycles. It guides projects through defined phases—from requirements gathering and planning to architecture, implementation, testing, and deployment. It maintains strict context discipline by summarizing agent outputs and referencing full artifacts, ensuring efficient, structured, and non-overwhelming communication to the user.

Alex converts high-level requirements into a precise, dependency-aware, and actionable implementation checklist. It maps critical paths, breaks down features into atomic micro-tasks, and establishes clear Definitions of Done (DoD) across various layers. Ideal for bridging the gap between conceptual goals and step-by-step engineering execution.