Skill UI

A comprehensive scaffold generator for building Model Context Protocol (MCP) servers using TypeScript and Node.js. This tool provides a complete, production-ready boilerplate, including proper project structure, dependency management (e.g., @modelcontextprotocol/sdk, zod), and implementation details for tools and resources. It supports both HTTP and stdio transport mechanisms, ensuring type safety, robust error handling, and best practices for modern backend development.

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 skill helps design the complete user journey or 'flow' before building individual screens. It applies established UX principles (like progressive disclosure and hub-and-spoke models) to define entry points, exit paths, and the overall navigation architecture. Use it for complex tasks like onboarding, checkout, or multi-step features to ensure a coherent, logical, and highly intuitive user experience.

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 comprehensive guide serves as a technical reference for migrating applications from the Universal Windows Platform (UWP) framework to the modern WinUI 3 / Windows App SDK. It details crucial API changes, including namespace updates, window management (CoreWindow to AppWindow), threading models (CoreDispatcher to DispatcherQueue), and best practices for dialogs and background tasks. Essential for developers updating legacy UWP codebases.

A comprehensive guide providing Git workflow standards specifically adapted for domestic Chinese development platforms (like Gitee, Coding, etc.). It covers best practices for adopting development models (Trunk-Based, Git Flow), standardized branch naming conventions, Chinese Conventional Commits, and adapting CI/CD pipelines for local environments, ensuring efficient and stable team collaboration.

This tool generates canonical, submit-ready FrontendIntegration YAML from a simplified, single-menu authoring model. It is ideal for converting preliminary configurations (like those derived from a CRD) into the full, structured YAML required for deploying frontend integrations, ensuring all fields and placements are correctly mapped.

This specialized coach provides comprehensive guidance for finance professionals, covering diverse fields such as investment banking, private equity, asset management, and fintech. It teaches core technical skills (DCF, LBO, 3-statement modeling) and prepares users for rigorous industry recruiting, including case studies and technical interviews. It addresses both Chinese and global financial markets, helping users strategically navigate career barriers and build professional expertise.

This coach guides users through a progressive mastery of AI, structured across three layers: AI User (prompting), AI-Enhanced Worker (workflow integration), and AI Builder (ML foundations). It uses Socratic questioning, layered scaffolding, and spaced repetition to build genuine, transferable AI literacy, moving beyond simple copy-pasting prompts to deep conceptual understanding of models, transformers, and RAG.

Astropy is a Python library for astronomical research that handles unit-aware quantities, celestial coordinates, FITS files, cosmological models, precise time systems, and tabular data, letting you transform, convert, and analyze observational or theoretical datasets.

COBRApy is a Python library for constraint-based reconstruction and analysis of genome-scale metabolic models, letting you load SBML/JSON/YAML models, perform FBA/FVA/sampling, and study gene or reaction knockouts for systems biology and metabolic engineering.

DeepChem is a Python library for molecular machine learning, providing featurizers, dataset loaders, splitters, and models spanning traditional ML to GNNs for property prediction, drug discovery, and materials design.