Skill UI

This skill generates comprehensive unit and integration tests using Jest for JavaScript and TypeScript. It supports advanced testing patterns, including deep mocking, asynchronous handling, snapshot testing, and testing React components with Testing Library. Use it when the user needs to thoroughly validate application logic and behavior.

This library provides a comprehensive framework for managing bounded AI workflows (loops). Users can analyze existing codebases or work histories to discover opportunities for repeating, automated agent processes. It guides the user through finding published templates, auditing and repairing weak agents, adapting parameters, or crafting entirely new, robust automation designs, ensuring clear triggers, verification, and mandatory stopping conditions.

A comprehensive skill for generating keyword-driven automated test scripts using Robot Framework. It supports front-end UI testing via SeleniumLibrary, robust API integration using RequestsLibrary, and complex custom keywords. Ideal for building end-to-end and functional test suites.

This skill enforces highly efficient coding practices, guiding the AI to write the simplest, most direct code possible. It strictly follows an 'Efficiency Ladder' (YAGNI, reusing existing code, standard library, etc.) to prevent over-engineering, unnecessary abstractions, and boilerplate. Use this when the goal is maximum efficiency with minimal code.

Generates a precise, evidence-based installation plan for a project's component catalog (ECC). Instead of loading a full, noisy bundle, this skill analyzes the repository's actual code structure, dependencies, and CI/CD evidence to classify skills, rules, and commands into 'DAILY' (essential) and 'LIBRARY' (referenceable) buckets. It ensures only necessary components are active, optimizing the development environment.

A comprehensive, library-agnostic checklist covering best practices for Flutter and Dart applications. It guides developers on architectural principles, state management patterns (BLoC, Riverpod, etc.), Dart idioms, performance optimization, and clean code principles, making it invaluable for code reviews.

This library provides comprehensive patterns for building robust Clinical Decision Support Systems (CDSS) integrated into Electronic Medical Records (EMR). It ensures patient safety by implementing critical checks such as drug interaction screening, weight/age/renal-adjusted dose validation, and standardized clinical scoring (e.g., NEWS2). The engine uses pure functions, guaranteeing high testability and reliability for critical medical insights.

Manim is a powerful Python library used for creating high-quality mathematical and scientific animations. This skill facilitates the generation of educational videos and complex visualizations for presentations. It is ideal for researchers, educators, or content creators who need to visualize complex concepts in an engaging animated format. Note: This specific file is related to localization/translation.

This file serves as the dedicated translation resource for the MCP Server Patterns library. It is used to localize key strings and texts to ensure accurate user experience across different languages. Developers should refer to the original skill file for the core English content.

This skill provides robust automation capabilities for interacting with network devices via SSH using the industry-standard Netmiko library. It is designed to simplify complex network operations, such as configuration backups, status checks, and bulk management across heterogeneous devices, significantly improving operational efficiency.

This guide provides comprehensive patterns for behavior-focused testing of React components, custom hooks, and pages. It covers best practices using React Testing Library (RTL), including accessibility assertions, simulating user interactions (userEvent), and network mocking with MSW. It also helps developers define clear test boundaries between unit component tests and full E2E flows.

This tool scans your entire content library to detect decay signals—such as declining traffic, falling rankings, outdated stats, and lost AI citations. It then prioritizes content refreshes by calculating the potential revenue impact, ensuring you focus on updating the pieces that will recover the most revenue first.