Skill UI

Runs automated visual regression tests via Percy, Chromatic, or BackstopJS to capture component or page screenshots, compare them against baselines, and flag unintended UI changes during feature updates or CI/CD runs.

This skill guides the detection of unintended visual changes in user interface (UI) components. It works by capturing screenshots and performing pixel-by-pixel comparisons against approved baselines. It is essential for ensuring UI consistency across code changes, responsive breakpoints, and differing environments. Supports modern tools like Playwright, Percy, and BackstopJS.

An expert specialist for rigorous visual validation, focusing on UI testing, design system compliance, and WCAG accessibility verification. This skill systematically analyzes visual evidence for cross-platform consistency, pixel-perfect accuracy, and adherence to modern design standards, utilizing principles from visual regression testing and comprehensive QA methodologies.

A comprehensive toolkit for automating and testing local web applications using Python and Playwright. It supports complex dynamic interactions, managing server lifecycles, identifying stable selectors, capturing visual evidence (screenshots), and debugging UI behavior. The guides follow a robust 'Reconnaissance-Then-Action' pattern for reliable end-to-end testing.

This skill provides a comprehensive framework for performing end-to-end UI testing and verification on Android emulators. It guides users through systematic processes including device calibration, UI element discovery using uiautomator, performing complex interactions (tap, swipe, input), and robust reporting via visual screenshots and logcat analysis. Ideal for ensuring the stability and functional correctness of React Native or native Android applications.

AWT provides AI coding tools with the ability to perform sophisticated End-to-End (E2E) web testing using a real browser. It accepts declarative YAML scenarios and executes them via Playwright. Key features include visual matching (OpenCV + OCR) instead of relying on fragile DOM selectors, automatic platform detection (React, Vue, etc.), and structured failure diagnosis for improved AI coding workflows.

Coordinated end-to-end testing workflow that leverages Playwright for browser automation, visual regression comparisons, cross-browser coverage, and CI/CD integration so teams can validate web apps before release.

Automates comprehensive end-to-end Quality Assurance (QA) for deployed web applications. This skill performs multi-stage testing, including smoke tests, complex user interactions (forms, authentication), visual regression checks across breakpoints, performance monitoring (Core Web Vitals), and WCAG accessibility audits.

A general-purpose skill for writing and executing end-to-end (E2E) browser automation tests using Playwright. It handles complex scenarios such as login flows, form submission, responsive viewport testing, and checking for broken links. The skill automatically detects local development servers and executes custom scripts in a safe, temporary directory, providing real-time debugging and results visualization.

This skill provides expert-level control over a live Chrome browser. It enables full web automation (clicking, form filling, navigation), deep debugging (analyzing console logs, network requests), visual inspection (screenshots, snapshots), and detailed performance profiling. Ideal for QA engineers, web scraping, and front-end development testing.

Defines Salesforce Lightning Web Component, Aura, and Visualforce quality/security standards covering SLDS 2 styling, accessibility (WCAG 2.1 AA), data access patterns, component communication rules, XSS/CSRF protections, view state management, and Jest testing requirements for guided UI development and reviews.

A comprehensive tutor designed to build robust statistical thinking. It guides users through the entire data analysis lifecycle, from exploratory data visualization and descriptive statistics to complex hypothesis testing and regression modeling. The focus is on understanding the 'why' and 'how' of statistical logic using real datasets and practical problems, rather than just memorizing formulas. It is tool-agnostic, supporting concepts learned in Python, R, SPSS, or Stata.