Skill UI

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 comprehensive multi-agent feedback loop inspired by Generative Adversarial Networks (GANs), designed to build complex, production-ready applications. It separates generation from evaluation, using a 'ruthlessly strict' evaluator (QA) to drive continuous iteration. This process ensures high design quality, robust functionality, and originality, overcoming the limitations of single-agent self-critique. Ideal for full-stack feature development and complex product specification implementation.

Leverage AI-powered exploratory testing to thoroughly audit web applications and verify functionality. This skill simulates user interaction, detecting complex bugs, validating critical flows (like checkout or login), and checking accessibility compliance. Use it proactively after development to ensure quality before deployment.

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.

This skill guides the end-to-end process of building automated evaluation pipelines for Python applications powered by Large Language Models (LLMs). It teaches how to define evaluation criteria, instrument the application, build golden datasets, and run real-world evaluations using tools like pixie, ensuring the app's logic (not just the LLM) is rigorously tested. Ideal for QA, benchmarking, and improving LLM-based services.

This guide maps user questions and topics (e.g., configuration, memory, skills, CLI) to specific, relevant documentation paths and source code entry points within the QwenPaw ecosystem. It is designed for built-in QA Agents to eliminate aimless searching, ensuring the agent reads the most accurate files to provide comprehensive and precise answers about the system's features.

Google Quantum AI's open-source Python framework for designing, simulating, and running quantum circuits. Build NISQ circuits, model noise, run parameter sweeps, optimize circuits, and execute on Google, IonQ, AQT, Pasqal, or Azure Quantum backends.

This utility provides a structured way to manage and separate notes across multiple accounts and environments within Apple Notes. By utilizing distinct accounts (e.g., iCloud for Production, 'On My Mac' for Development), users can ensure absolute data isolation between development, staging, and live production data. It is crucial for maintaining clean separation in complex development workflows and QA testing scenarios.

A comprehensive diagnostic tool designed to collect all necessary evidence for troubleshooting persistent Figma REST API issues or preparing detailed support tickets. It automatically tests connectivity, checks rate limit status, captures environment information, and redacts sensitive data, providing a clean, actionable archive for developers and QA teams.

This comprehensive debugging bundle collects essential diagnostic evidence from Linktree link-in-bio API integrations. It systematically checks OAuth token validity, profile metadata retrieval, link status, click analytics availability, and webhook endpoint health. The resulting data package helps support engineers quickly diagnose complex issues like broken links, missing analytics events, and permission errors without needing direct Linktree admin access. Ideal for QA and support teams.

A comprehensive index of 46 production-grade test automation skills, covering E2E, unit, mobile, BDD, and cloud testing across 15+ languages. This skill equips AI coding assistants to act as senior QA architects, generating structured, real-world test code for major frameworks like Playwright, Selenium, Cypress, and Jest. It supports project setup, CI/CD integration, and cross-platform debugging.

This skill facilitates an interactive Quality Assurance (QA) session. Users can conversationally describe encountered bugs or issues. The agent clarifies the problem, explores the codebase for context and domain language, and then meticulously files durable GitHub issues. It supports both single-issue reports and breaking down complex problems into multiple, independently verifiable tickets, ensuring developers receive actionable, high-quality reports written from the user's perspective.