Accessibility testing for web applications using Playwright (@playwright/test) with TypeScript and axe-core. Use when asked to write, run, or debug automated accessibility checks, keyboard navigation tests, focus management, ARIA/semantic validations, screen reader compatibility, or WCAG 2.1 Level AA compliance testing. Covers axe-core integration, POUR principles (perceivable, operable, understandable, robust), color contrast, form labels, landmarks, and accessible names.
To ensure the application is usable by people with disabilities, complying with WCAG standards and improving SEO/UX. Use when: During development of UI components; Before major releases.
Accessibility testing toolkit using Selenium WebDriver 4+ with Java 21+ and axe-core engine. Use when asked to validate WCAG 2.1/2.2 compliance, scan pages or components for a11y violations, test keyboard navigation, audit color contrast, check ARIA semantics, generate accessibility reports, filter axe rules, debug screen reader issues, or implement POUR principles (perceivable, operable, understandable, robust).
To allow incompatible interfaces to work together by wrapping an object in an adapter that translates its interface into one that a client expects. Use when: When integrating a third-party library whose interface doesn't match your application's internal requirements; When you want to standardize multiple different implementations of a service (e.g., different payment gateways); When you need to provide a stable interface while the underlying dependency is subject to change.
To implement a scalable permission system where users have roles, and roles have granular permissions. Use when: B2B SaaS applications (Admin, Editor, Viewer); Systems with complex access requirements.
Browser automation CLI for AI agents. Use for website interaction, form automation, screenshots, scraping, and web app verification. Prefer snapshot refs (@e1, @e2) for deterministic actions.
Visual UI annotation tool for AI agents. Drop the React toolbar into any app — humans click elements and leave feedback, agents receive structured CSS selectors, bounding boxes, and React component trees to find exact code. Supports MCP watch-loop, platform-specific hooks (Claude Code / Codex / Gemini CLI / OpenCode), webhook delivery, and autonomous self-driving critique with agent-browser.
To structure autonomous AI systems that can reason, plan, and execute tools to solve complex, multi-step problems using patterns like ReAct and Multi-Agent orchestration. Use when: When the task requires multiple distinct steps (e.g., "Find the price of BTC and email me the summary"); When the LLM needs to interact with the outside world (APIs, Databases, Web Search); When the workflow is non-linear and depends on intermediate results.