Skill UI

This guide details how to configure SMTP email delivery settings within a Webiny project using the `<Api.Mailer.Smtp />` component. It specifies the required properties (host, port, user, etc.) and emphasizes best practices, such as passing sensitive passwords via environment variables rather than hard-coding them. Ideal for developers setting up external email services.

This skill provides comprehensive testing for WebSocket APIs, covering protocol basics, Cross-Site WebSocket Hijacking (CSWSH), and common security flaws. Use it when testing applications that rely on real-time communication, chat features, or persistent data streams. It validates Origin checks, session binding, and message integrity.

This skill guides the creation of production-grade Model Context Protocol (MCP) servers and basic clients using C# and the .NET framework. It ensures adherence to the stable 2.x specification, addressing common pitfalls like stateful/stateless HTTP defaults, handling deprecations (roots, sampling), and utilizing appropriate transports (STDIO, Streamable HTTP). Essential for developers integrating structured AI APIs into enterprise applications.

This guide provides a comprehensive workflow for searching and retrieving biomedical literature from PubMed and NCBI. It covers advanced query construction using MeSH terms and specific field tags, advanced filtering (date, publication type), and a repeatable programmatic workflow using NCBI E-utilities (esearch, efetch). Essential for researchers conducting systematic reviews and literature monitoring.

This tool automatically converts design screenshots or visual exports into structured, functional Vue 3 Composition API components. It supports popular UI libraries like Vant, Element Plus, and Ant Design Vue. By batch converting screenshots, it streamlines the development process from raw design assets to reusable, production-ready code, significantly boosting frontend development efficiency.

This comprehensive skill facilitates deep integration with X (Twitter) data, supporting advanced scraping, user lookup, and data extraction. It provides multiple integration patterns for developers: dedicated SDKs (Python, TypeScript, Go, etc.), REST endpoints, hosted Apify Actors, and agent automation via MCP. Ideal for building robust data pipelines, monitoring systems, and AI-powered applications requiring real-time X data.

This guide provides comprehensive, structured error handling patterns for modern applications using TypeScript, Python, and general architectural principles. It covers essential concepts like typed custom errors, Result patterns, API error transformers, and React Error Boundaries. Learn how to implement fail-fast mechanisms, differentiate user-facing messages from developer logs, and ensure system resilience against unexpected failures.

A comprehensive CI/CD pipeline for Quarkus applications, designed to verify code quality and deployment readiness. It covers all phases: build, static code analysis (SonarQube, Checkstyle), rigorous functional testing (Unit, Integration, API), security scanning (OWASP, dependency checks), native image compatibility testing, performance load testing (K6), and container image validation (Trivy, Grype).

An expert AI specializing in Slang shader programming for high-performance graphics and compute applications. This model excels at writing, reviewing, refactoring, and optimizing production-grade code for various graphics stages (vertex, fragment, compute, ray tracing, tessellation). It ensures cross-platform compatibility across major APIs (D3D12, Vulkan, Metal, OpenGL) and supports advanced concepts like generics, parameter blocks, and automatic differentiation. Use this skill when integrating shaders into C++ engines or requiring deep knowledge of low-level GPU pipelines.

A comprehensive guide and toolkit for performing End-to-End (E2E) testing on native Windows desktop applications (WPF, WinForms, Win32, Qt) using pywinauto and the Windows UI Automation (UIA) API. Covers setup, locator strategies, and page object modeling for robust desktop automation.

This rigorous review simulates an interrogation by a Chief AI Officer (CAIO). It pressure-tests any AI plan across six critical dimensions: evaluation metrics, failure mode handling (SLOs), international regulatory compliance (e.g., EU AI Act), technical architecture (API vs. fine-tuning), cost economics, and necessary team roles. Use this when ensuring AI features are robust, safe, and commercially sound before launch or major investment.

Provides C-level strategic guidance for startups deciding on advanced AI implementation. Covers critical decisions including model build-vs-buy analysis (API vs. fine-tune vs. in-house), regulatory compliance risk assessment (EU AI Act, NIST RMF), economic modeling for self-hosting breakeven, and optimal AI team scaling. This is a high-level advisory tool, not a tactical engineering guide.