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.

This tool scaffolds a complete, production-ready Model Context Protocol (MCP) server project written in Rust. It utilizes the official `rmcp` SDK to ensure full compatibility. The generated structure includes modules for handling logic, defining tools, managing prompts, and providing resources. Users can configure different transports (stdio, SSE, HTTP) and immediately run the server for integration testing or production use.

This skill guides the integration of Arize AX tracing into existing applications, especially complex LLM applications. It follows a structured, two-phase, agent-assisted flow: first analyzing the codebase (read-only) to understand dependencies, and then implementing instrumentation. It supports multi-language environments (Python, TypeScript, Java, Go) and integrates deeply with OpenTelemetry and various LLM providers, ensuring comprehensive observability without altering core business logic.

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.

Build agentic applications by embedding GitHub Copilot workflows into apps using Python, TypeScript, Go, or .NET. Create custom tools, manage sessions, and implement streaming responses without building orchestration logic. Ideal for developers integrating AI agents into existing software.

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.

This skill guides the end-to-end process of building automated evaluation pipelines for Python applications powered by Large Language Models (LLMs). It teaches how to define evaluation criteria, instrument the application, build golden datasets, and run real-world evaluations using tools like pixie, ensuring the app's logic (not just the LLM) is rigorously tested. Ideal for QA, benchmarking, and improving LLM-based services.

This skill focuses on the 'Remove Parameter' refactoring pattern specific to Java development. It teaches systematic techniques to analyze methods and identify unused or redundant parameters. By applying this refactoring, developers can significantly improve code readability, enhance maintainability, reduce coupling, and simplify method signatures while preserving original functionality and logic.

This skill provides comprehensive guidelines and techniques for safe code refactoring. It focuses on improving code structure, readability, and maintainability without altering the external behavior or functionality of the system. Learn to address common code smells, such as long methods, duplicated logic, god objects, and primitive obsession, using established design patterns and best practices. It emphasizes that refactoring should be gradual, test-driven, and methodical.

A comprehensive, five-stage methodology for reviewing code quality. This process systematically checks for requirements compliance, logical correctness, code maintainability, test coverage, and critical security/performance vulnerabilities. It provides actionable feedback categorized by severity (Blocker, Major, Minor, Nit), ensuring high standards for all merged code.

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.

A comprehensive guide and utility for managing API interactions with BambooHR. It implements robust retry logic (with exponential backoff and jitter) to handle 503 and 429 rate limit errors. It also provides patterns for proactive optimization, such as queue-based throttling, batching data using custom reports, and utilizing incremental sync to maximize API throughput and reliability.