Skill UI

This skill helps generate comprehensive and structured TestNG test cases for Java. It supports advanced testing features crucial for enterprise automation, including method grouping, data-driven testing using @DataProvider, parallel execution setup, XML suite configuration, and custom listeners. Use it when building robust, scalable, and maintainable automated test suites.

This skill generates comprehensive unit and component tests using Vitest, a fast testing framework built for Vite. It supports JavaScript and TypeScript, offering a Jest-compatible API with advanced features like ESM support, HMR, advanced mocking (vi.mock), snapshot testing, and React component rendering. Use it when dealing with unit testing, TDD, or configuring vitest.config.ts.

This skill generates comprehensive end-to-end (E2E) automation tests using WebdriverIO (WDIO) in JavaScript or TypeScript. It supports various frameworks (Mocha, Jasmine, Cucumber) and includes advanced patterns like the Page Object Model (POM) and robust selector strategies. Users can configure tests for local execution or integrate with cloud services like LambdaTest, making it ideal for simulating complex user interactions and ensuring application quality across multiple browsers and environments.

A comprehensive three-column layout template designed for high-readability technical documentation, such as API references, tutorials, and guides. It features a sticky side navigation, main article body with support for code blocks and callouts, and a scroll-spy Table of Contents (TOC). Includes search and version control.

This skill drives an existing browser instance using the Chrome DevTools Protocol (CDP). It is essential for complex web automation tasks that require visible interaction, JavaScript execution, or handling authenticated, stateful sessions. Use it when static data fetching is insufficient, such as simulating user logins, interacting with iframes, or performing visual verification. Supports headless and remote environments.

A comprehensive utility for managing complex cmux topologies, including workspaces, panes, and surfaces. This tool is essential for inspecting, controlling, and monitoring multiple AI coding agents running in parallel within a macOS terminal environment. It supports advanced functionalities such as browser automation and real-time status notifications, optimizing developer workflows.

This guide outlines how to build new API connectors or providers that strictly adhere to the existing integration architecture and patterns of a target repository. It is designed for adding integrations while maintaining architectural consistency (e.g., layout, configuration schema, authentication model, and testing style), preventing the creation of novel, unsupported patterns.

This utility executes an autonomous, iterative loop, simulating a comprehensive agent workflow. It systematically manages tasks by checking current status, predicting the optimal next action using specialized tools, executing the required task (e.g., coding, testing), logging the progress, and scheduling the next iteration. It supports task discovery from multiple sources, including team tasks, swarm tasks, and file checklists, ensuring continuous, automated progress towards a defined goal.

Automates the process of filling out web forms using a structured field map (field_name → value). This tool supports using stored templates for known forms, or falling back to analyzing the page accessibility snapshot if no template exists. It includes mandatory PII screening (aidefence_has_pii) and handles form submission, making it ideal for automating sign-ups, checkouts, and data entry tasks.

This skill monitors for regressions on deployed URLs following deployments, merges, or dependency upgrades. It performs comprehensive checks including HTTP status, console errors, network failures, performance metrics (LCP/CLS/INP), and crucial API endpoint health, ensuring service stability in production or staging environments. Supports quick checks, continuous monitoring, and differential comparison against baselines.

Automates the scaffolding of a complete Aggregate Root structure within a specified Bounded Context, adhering strictly to Domain-Driven Design (DDD) principles. It generates necessary files including the entity, supporting value objects, repository interface, domain event definitions, and comprehensive unit test stubs, significantly accelerating the modeling of new business concepts and enforcing domain invariants.

A formal framework designed for evaluating AI-assisted code generation sessions. It implements Development Oriented Evals (EDD) principles, allowing users to define capability and regression criteria. The system supports multiple evaluation types—code-based, model-based, and human review—and tracks reliability using advanced metrics like pass@k, ensuring robust quality control for AI agents.