Skill UI

This generator provides a comprehensive, production-ready template for building a Model Context Protocol (MCP) server using Kotlin. It sets up the full Gradle structure, integrates essential dependencies (MCP SDK, Ktor, Coroutines), and includes robust placeholders for custom tools, error handling, and testing, accelerating the development of context-aware backend services.

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.

This agent guides users in selecting the optimal Azure role assignment that strictly adheres to the principle of least privilege. Given a set of desired permissions, it utilizes specialized tools to either identify a suitable built-in role or generate a custom role definition. Finally, it provides actionable code snippets, including CLI commands and Bicep templates, ensuring proper and secure role assignment.

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.

Creates a production-ready Model Context Protocol server in Go by scaffolding modules, tools, resources, configuration, README guidance, and basic tests built around the official GitHub SDK for rapid setup.

Generates a complete, compilable xUnit integration test project structure for validating complex database migrations, specifically from Oracle to PostgreSQL, within .NET solutions. This scaffolding includes a transaction-rollback base class and a seed data manager, ensuring a robust environment for writing migration integration tests without requiring test cases. Ideal for setting up testing infrastructure before migration validation.

PyTDC is a specialized Python package designed to interact with the Therapeutics Data Commons. It facilitates the discovery of machine learning tasks, loading approved datasets, generating task-appropriate data splits, and evaluating predictions for drug discovery research. The tool strongly emphasizes performing metadata-only discovery and planning before initiating resource-intensive data downloads or network operations.

The Hugging Face Transformers library is a comprehensive toolkit providing access to thousands of pre-trained models across diverse domains including Natural Language Processing (NLP), Computer Vision, Audio, and Multimodal tasks. It enables developers to easily load models, perform high-level inference (e.g., text generation, classification, QA), and fine-tune models on custom datasets using streamlined APIs like Pipelines and the Trainer class. Ideal for building advanced, state-of-the-art AI applications.

This skill provides a comprehensive workflow to export images, icons, and assets directly from Figma files using the REST API. It enables users to render specific frames or components as high-quality PNG, SVG, JPG, or PDF files. Ideal for building automated asset pipelines, generating documentation previews, or batch exporting large icon libraries.

This comprehensive guide details setting up a secure Canva Connect API integration using the OAuth 2.0 Authorization Code flow with PKCE (Proof Key for Code Exchange). It covers the entire lifecycle: client registration, token generation, code exchange for access tokens, and refreshing expired tokens. Use this skill when building applications that require programmatic access to Canva's design and asset management capabilities.

This integration automates the critical process of converting design assets from Figma into production-ready code using Anima. It establishes a full CI/CD pipeline via GitHub Actions, enabling scheduled or manual component generation. Use this when automating design system handoffs, maintaining code synchronization with design changes, or implementing robust, reliable development workflows.

A comprehensive checklist for validating the production readiness of design-to-code pipelines, specifically for Anima's service converting Figma designs into React, Vue, and HTML. Use this guide when deploying automated code generation services, setting up CI/CD for design handoffs, or validating output quality to ensure component integrity and prevent production failures.