Skill UI

A comprehensive red-teaming skill for assessing the security posture of Retrieval-Augmented Generation (RAG) pipelines. It systematically probes two critical injection surfaces: poisoned retrieved context (indirect prompt injection) and embedding manipulation. Use this skill to validate retrieval guardrails, ensure data leak prevention, and demonstrate vulnerabilities in LLM-powered knowledge assistants.

This skill generates robust, production-grade Appium automation scripts for both Android and iOS. It supports multiple languages (Java, Python, JavaScript) and guides users through best practices for cross-platform testing, including proper locator strategies, explicit waiting, and complex gestures. Ideal for automating mobile QA processes on local emulators or cloud real device farms.

A comprehensive, four-phase methodology for debugging complex software issues. It guides users through root cause investigation, pattern analysis, hypothesis testing, and controlled implementation. The core principle emphasizes finding the underlying cause rather than applying quick, symptomatic fixes, ensuring robust and reliable code improvements across various failure scenarios.

Provides comprehensive guidelines and utilities for modern unit testing using Jest and React Native. Covers Test-Driven Development (TDD) workflows, Behavior-Driven Development (BDD) principles, advanced mocking strategies for modules and GraphQL hooks, and implementing reusable factory functions for props and data. Use this skill to write robust, maintainable, and highly structured unit tests.

This skill manages the end-to-end lifecycle of cloud test execution using HyperExecute for platforms like LambdaTest/TestMu AI. It provides comprehensive functionality to analyze projects, generate and validate complex YAML configurations, run CLI jobs, and debug failures. Ideal for integrating robust, scalable, and matrix-based testing into CI/CD pipelines.

This skill provides comprehensive guidance on choosing and creating the appropriate Neon Postgres branch for development and testing workflows. Users must decide between a 'Normal' branch (for realistic migration testing with live data) or a 'Schema-Only' branch (for highly sensitive data environments). It details the decision rules, tool selection (CLI/MCP), and procedures for branch creation and resetting.

A powerful skill for generating scripts using Puppeteer, enabling robust browser automation, data scraping, and PDF creation. Use this when you need to simulate user interactions, perform complex web testing, or extract content from dynamic websites, including handling wait strategies, network interception, and generating media files.

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.

A comprehensive guide detailing industry-standard best practices for building scalable and maintainable Flutter/Dart applications. This skill covers architectural patterns (MVVM, feature-based design), state management, coding standards (modern Dart features, widget usage), and testing methodologies. Use it for planning, structuring new features, reviewing existing codebases, or refactoring complex logic into robust, testable components.

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.

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.

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.