Skill UI

Automatically detects and loads specialized agent instructions from the `AGENTS.md` file in the current working directory. This skill seamlessly integrates project-specific rules and behavioral guidelines into the AI's session context, ensuring the AI operates according to project standards without manual intervention. It is crucial for maintaining specialized agent behaviors across different development tasks and directory changes.

A comprehensive guide detailing best practices for creating modern, clean, and highly maintainable user interfaces using Avalonia and Zafiro.Avalonia. It emphasizes leveraging semantic containers, defining shared styles, and utilizing generic components to dramatically reduce XAML redundancy and improve code structure. Learn to adopt patterns like Behaviors over Converters, and prevent deep nesting for optimal developer experience.

This skill defines the mandatory conventions and architectural rules for developing maintainable, cross-platform applications using Avalonia UI and the Zafiro toolkit. It enforces a functional-reactive MVVM approach, prioritizing explicit error handling via Result types, and mandates leveraging Zafiro's existing abstractions to ensure code quality and predictability.

This comprehensive guide covers universal coding standards, best practices, and architectural patterns for modern web development using TypeScript, JavaScript, React, and Node.js. It emphasizes fundamental principles like Readability, DRY, and Immutability, while providing detailed guidelines on component structure, state management, asynchronous programming, and designing robust REST APIs.

A comprehensive template for developing modern, full-stack applications. This guide covers architecture overview, file structure, and best-practice code patterns for advanced technology stacks including Next.js, FastAPI, Supabase, and structured AI integration using Claude API. It serves as a robust blueprint for professional development teams.

A comprehensive set of architectural and coding standards for building high-reliability, observable, and maintainable backend services. It mandates the use of layered architecture, explicit error handling (Sentry), robust validation (Zod), and strict dependency management for Node.js, Express, and TypeScript projects.

Applies Anthropic’s official color palette, typographic hierarchy, and accent system to any artifact that needs the company’s visual identity, ensuring consistent styling across materials with smart fallbacks for system fonts.

This skill provides access to official Anthropic brand identity resources, ensuring visual consistency across all documents. It automatically applies defined brand colors (primary, accent, neutral) and typography (Poppins for headings, Lora for body text) while maintaining structural integrity and readability. Ideal for professional reports, presentations, and marketing materials needing strict corporate adherence.

This skill provides access to Anthropic's official brand identity and style resources. It ensures consistent and professional visual branding by managing defined primary and accent color palettes, and applying specific typography rules (e.g., Poppins for headings, Lora for body text). It guarantees that the output maintains a cohesive, on-brand appearance across various systems.

This skill provides comprehensive guidelines based on Clean Code principles, transforming merely functional code into highly readable, maintainable, and robust software. It covers best practices for naming conventions, function design (small, single responsibility), error handling (exceptions over return codes), formatting, and adhering to Test-Driven Development (TDD). Use this skill when writing new features, reviewing pull requests, or refactoring legacy systems to improve overall code quality.

This skill guides users on fine-tuning CodeRabbit's review quality. It teaches the AI agent team preferences through PR feedback, integrates custom code guidelines (e.g., security policies), allows customization of the review tone, and implements path filters to drastically reduce false positives and noise. This ensures that all code feedback is highly relevant, actionable, and aligns with specific team standards.

Behavioral rules for LLMs writing or reviewing code, emphasizing caution, simplicity, surgical edits, and goal-driven execution to avoid common mistakes during implementation tasks, bug fixes, or refactors.