Skill UI

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.

This skill provides the central management layer for defining an AI agent's capabilities. It uses the `capabilities.yaml` file as the single source of truth to configure everything from integrated skills, tools, hooks, and sub-agents. Use this when running any `capa` command (install, clean, add, etc.), integrating a new service, or troubleshooting the project's entire operational environment.

KubeEye is a comprehensive cluster inspection tool designed for Kubernetes environments, particularly within KubeSphere. It systematically detects issues across workloads, nodes, and configurations by integrating multiple policy engines, including OPA/Rego, PromQL, file integrity checks, kernel parameter validation, and systemd health checks. Users can define custom inspection rules (InspectRule), schedule checks (InspectPlan), and retrieve detailed reports (InspectResult) to ensure cluster compliance and stability.

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.

Execute discrete, bounded coding, review, or verification tasks using the Codex CLI as an isolated subagent. This tool is ideal for tasks requiring explicit file ownership, such as isolated refactoring, feature implementation, or independent code verification, without relying on continuous conversational context. It runs in a safe sandbox environment.

This skill acts as a critical reality-check layer before code generation. It ensures that every function call, import, method, and variable the agent uses is provably available in the user's specific installed environment and version. It prevents runtime bugs caused by hallucinated or improperly assumed APIs, making the generated code robust and reliable.

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.

This model provides an engineering operational framework for teams that leverage AI agents to generate large volumes of code. It shifts the focus from simple syntax to process quality, robust architecture design, rigorous testing, and systematic risk control, ensuring the development of highly stable and reliable systems in AI-assisted environments.

This harness transforms standard code environments into fully persistent, autonomous agent systems. It leverages native features like scheduled tasks (Crons), remote dispatching, and structured memory storage (MCP) to enable continuous, self-directing automation. Ideal for building AI assistants that require long-term context, recurring workflows, and complex, multi-step operations, eliminating the need for external agent frameworks like AutoGPT.

A comprehensive skill for automated UI testing. It records complex user interactions, navigates web pages, and validates functionality by producing a replayable RVF artifact. Use it to verify user flows, detect regressions against baseline sessions, and re-run complex tests in a real browser environment.

Bun is a high-performance, all-in-one JavaScript runtime and toolchain. It functions as a runtime, package manager, bundler, and test runner, offering significant speed improvements over traditional Node.js setups. It is ideal for modern, fast-paced development, new projects, and deployment environments like Vercel.