Skill UI

Guidelines for building modern, maintainable Avalonia UI layouts with Zafiro.Avalonia, covering shared styles, semantic containers, icons, behaviors, and generic components while avoiding XAML redundancy and anti-patterns.

This .NET SDK enables developers to manage Azure Application Insights resources for application performance monitoring and observability. It supports creating components, retrieving connection strings, generating API keys, and configuring availability tests via C# code. Ideal for DevOps and backend development workflows involving cloud monitoring.

A comprehensive guide detailing the implementation of the 'Bento UI' design pattern across web (CSS Grid), mobile (SwiftUI), and cross-platform (Flutter) environments. This pattern creates highly structured, modular, grid-based dashboards resembling modern app layouts, ensuring aesthetic consistency and responsiveness.

A comprehensive guide focused on mastering the Entity Component System (ECS) architecture within the Bevy game engine using Rust. Learn best practices for building high-performance, data-oriented game logic. This covers defining components, writing parallel systems, managing global resources, and optimizing queries for efficient, scalable game development.

A command-line utility to search and extract data from Burp Suite project files (.burp). It enables searching response headers or bodies with regex patterns, extracting security audit findings, and analyzing HTTP traffic. Requires Burp Suite Professional and a specific extension. Use sub-component filters to avoid large data dumps.

This skill generates comprehensive C4 architecture documentation for existing repositories using a bottom-up analysis approach. It follows the C4 model (Context, Container, Component, Code) to create technical documentation suitable for both technical and non-technical stakeholders. The workflow analyzes code directories, synthesizes components, and maps containers to provide a complete architectural view.

This skill specializes in creating comprehensive C4 code-level documentation for software projects. It analyzes code directories to extract function signatures, arguments, dependencies, and structural information. The output includes detailed documentation and Mermaid diagrams to visualize code relationships, dependencies, and data flows. Use this for documenting complex code components, understanding system architecture at the code level, and generating best practice checklists for code structure.

This skill specializes in creating C4 Component-level architecture documentation. It synthesizes code-level details into logical components, defining boundaries, interfaces, and relationships within containers. It generates Mermaid diagrams and best practices for software architecture documentation.

Generates C4 container-level documentation for system deployment, mapping components to containers, defining interfaces with OpenAPI specs, and producing Mermaid container diagrams.

This skill specializes in creating and maintaining Chrome Extensions using Manifest V3 architecture. It covers essential components like service workers, content scripts, and popup interfaces. The expert ensures secure cross-context communication and implements best practices for permissions and storage. Ideal for migrating legacy extensions or building new production-ready tools within the Chrome ecosystem.

This comprehensive skill serves as an entry point for all web UI design needs, covering components, pages, dashboards, and advanced layouts using React, Vue, or pure HTML/CSS. It addresses not only aesthetics and polish (color, typography, spacing) but also deep structural concerns like responsive design, UX auditing, and visual system implementation. Use it whenever a user asks to improve the look, feel, or function of a digital interface.

Use Clarvia AEO (Agent Experience Optimization) to score any MCP server, API, or CLI tool for agent-readiness. It evaluates 15,400+ AI tools across four dimensions—API accessibility, data structuring, agent compatibility, and trust signals—ensuring the components are robust enough for advanced AI agent workflows and dynamic selection processes.