Skill UI

This skill provides a structured, multi-step workflow for resolving conflicts encountered during in-progress Git merges or rebases. It guides users through analyzing conflict origins, preserving original development intents, resolving conflicting code hunks without introducing new behavior, and executing comprehensive automated checks (typechecking, testing, formatting) to ensure the resulting codebase is stable and functional.

A comprehensive skill for generating keyword-driven automated test scripts using Robot Framework. It supports front-end UI testing via SeleniumLibrary, robust API integration using RequestsLibrary, and complex custom keywords. Ideal for building end-to-end and functional test suites.

This skill generates comprehensive visual regression test configurations using SmartUI for accurate screenshot comparison on cloud platforms. It is framework-agnostic, supporting major tools like Playwright, Selenium, Cypress, and Puppeteer. Use it when detecting UI changes, ensuring visual consistency, or running automated visual tests across multiple viewports and browsers.

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.

A comprehensive guide covering standards for building highly maintainable Flutter applications using Dart. It details best practices across architecture (MVVM, layered design), state management (Cubit/BLoC), folder structure, widget implementation, and testing strategies. Use this skill when writing new code, reviewing existing codebases, or planning complex features to ensure scalable and clean development.

This comprehensive guide outlines a rigorous, human-in-the-loop engineering workflow for leveraging advanced AI agents to complete complex implementation tasks. It mandates defining clear completion criteria, decomposing work into manageable units, and strategically routing models (Haiku, Sonnet, Opus) based on task complexity. The workflow emphasizes continuous evaluation, regression testing, security review of invariants and edge cases, and strict cost discipline to ensure robust and reliable software development using AI assistance.

A comprehensive strategy for regression testing in AI-assisted development. It focuses on identifying systematic blind spots, such as schema inconsistencies between sandbox and production paths, and integrating automated bug checks into the development workflow. This guide teaches how to build robust, database-free API tests to ensure code quality when AI models contribute to the codebase.

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.

This skill allows for the deterministic re-running of recorded user session paths (trajectories). It is essential for regression testing, bug reproduction, and verifying UI flow consistency after deployments. Crucially, it utilizes browser-selectors embedding similarity to intelligently recover and retry interactions even when the underlying webpage structure (DOM) has drifted, providing robust testing capabilities.

Compares two recorded browser sessions step-by-step by matching steps via shared step IDs. It performs both pixel-level diffing on screenshots (for visual regression) and node-by-node diffing on accessible DOM snapshots (for structural changes). Ideal for visual regression testing, comparing A/B flow variants, and verifying replay consistency in UI development.