Skill UI

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.

Produces production-ready Dataverse SDK code for Python, showcasing advanced error handling and retries, batch operations, OData query tuning, metadata lifecycle management, configuration/timeouts, cache invalidation, chunked file uploads, and Pandas integrations with references.

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.

Guides architects through Dataverse SDK use cases by analyzing requirements, designing data models, selecting integration patterns, and producing production-ready Python code with best practices.

A structured methodology for coordinating multiple, simultaneous investigation threads to determine the root cause of complex system failures, performance regressions, or integration bugs. This skill decomposes a large problem into independent tracks (e.g., database, application code, infrastructure), assigns ownership, and manages structured sync points (Continue/Pivot/Converge) to converge on a solution faster than serial debugging. Essential for high-stakes production incident response.

A comprehensive tool for detecting, validating, and generating Schema.org structured data. It primarily focuses on the JSON-LD format, which is preferred for modern SEO best practices and enabling rich results. Use this when optimizing content markup, checking for validation errors, or generating structured snippets for articles, businesses, or products.

Generate production-quality React components and UIs using the specialized Rayden UI component library and Tailwind CSS. This skill ensures that all generated code strictly adheres to a defined design system, including correct component usage, design tokens, prop validation, and established layout patterns. Ideal for scaffolding complex pages like dashboards, login forms, and data tables while maintaining premium aesthetics and consistency.

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 guide provides a comprehensive, reproducible workflow for setting up an Algolia local development environment. It covers creating separate development indices, seeding mock data, and implementing client mocking for unit tests. This process ensures fast, safe iteration cycles, allowing developers to build and test search features without ever touching production data.

Provides comprehensive security guidelines for integrating Anthropic Claude API. Covers essential best practices including secure API key management (rotation, isolation), defending against prompt injection attacks by separating system instructions from user input, robust input sanitization, and output validation to prevent accidental data leakage. Essential for building production-grade, secure AI applications.

This utility provides a structured way to manage and separate notes across multiple accounts and environments within Apple Notes. By utilizing distinct accounts (e.g., iCloud for Production, 'On My Mac' for Development), users can ensure absolute data isolation between development, staging, and live production data. It is crucial for maintaining clean separation in complex development workflows and QA testing scenarios.

This guide provides a set of production-grade TypeScript patterns for robust interaction with the Attio REST API. It covers essential best practices including typed clients, automatic retry mechanisms with exponential backoff, cursor-based pagination iterators, and multi-tenant factory implementation, ensuring reliable and scalable API integration. Use these patterns to handle common API challenges like rate limiting and large datasets efficiently.