Skill UI

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.

This skill assists in generating robust Behavior Driven Development (BDD) tests. It supports writing feature files using Gherkin syntax (Given/When/Then) and automatically generates corresponding step definitions in multiple languages, including Java, JavaScript, and Ruby. Ideal for structuring and automating end-to-end functional tests.

A comprehensive skill for generating production-grade, robust automation scripts and end-to-end tests using Selenium WebDriver. Supports multiple languages (Java, Python, JS, C#, etc.) and execution environments, including local machines and TestMu AI cloud for cross-browser compatibility. It guides users on best practices like explicit waits, Page Object Model (POM), and stable locator strategies, acting as a senior QA automation architect.

Flox is a declarative, Nix-based environment manager designed to create perfectly reproducible development environments. It handles system-level dependencies (like compilers, databases, and native libraries) and ensures that complex toolchains across multiple languages (Python, Node.js, Go, etc.) function identically across macOS and Linux. Ideal for solving 'works on my machine' issues and onboarding new team members instantly.

RouterBase acts as a unified, OpenAI-compatible API gateway, allowing applications to route requests across diverse large language models (LLMs) like GPT, Claude, and Gemini. It helps developers migrate existing OpenAI SDK calls, implement sophisticated primary/fallback model selection based on cost, latency, and quality, and manage multimodal payloads (image, audio, video) from a single, standardized endpoint.

Perform a comprehensive technical audit of hreflang implementation critical for multilingual and multi-regional SEO. This tool checks for common errors, including missing self-referential tags, broken bidirectional links (A to B, but B doesn't point back to A), and invalid language-region codes. Essential for any brand operating international websites to prevent lost organic traffic.

Performs comprehensive audits on multilingual content to ensure parity, technical SEO compliance, and regional accuracy. It checks for missing translations, structural deviations, correct hreflang implementation, and adherence to global regulatory requirements (e.g., GDPR, DPDPA). Essential for global brands needing consistent user experience and high search visibility across multiple markets.

A comprehensive tool to configure and govern the multilingual settings for an active brand, controlling language infrastructure across translation, localization, and content auditing commands. Users can set primary/secondary languages, define do-not-translate terms, specify preferred translation services (DeepL, Google Cloud, etc.), and manage locale-specific formatting rules (date, currency, numbers). This ensures consistent, market-appropriate, and compliant multilingual output for global content workflows.

Intelligently translates marketing content across multiple languages, automatically selecting the optimal service (DeepL, Sarvam AI, Google Cloud, etc.). It goes beyond literal translation by performing transcreation analysis for cultural nuances, humor, and emotional impact, ensuring brand voice and marketing effectiveness are preserved globally. The process includes quality scoring and adherence to specific compliance rules and glossaries.

A comprehensive guide for authors writing the Related Work section of ACM Multimedia papers. It teaches how to position cross-modal contributions by covering diverse single-modality shelves (Vision, Audio, Language, HCI), tracing the cross-modal lineage, defining the specific cross-disciplinary novelty (delta), and adhering to academic hygiene standards like double-blinding and venue verification.

This skill evaluates and assembles the smallest, most efficient set of AI agent skills required to achieve a complex, natural language goal. It dynamically derives multi-step workflows, searching local indices, registries, and external sources using four specialized lenses (need, operation, outcome, method). It prioritizes user-friendly plain-language outputs while rigorously verifying compatibility, safety, and overlap between candidate skills before recommending the final stack.

This guide provides best practices for designing and auditing academic evaluations of programming language features and compilers, following standards used in venues like PLDI. It covers selecting defensible benchmark suites, establishing strong baselines, performing targeted ablations, and measuring multiple performance metrics (runtime, compile time, memory) to ensure research claims are reproducible and scientifically sound.