Skill UI

Master advanced testing strategies for smart contracts using industry-leading frameworks like Hardhat and Foundry. This skill covers unit testing, integration testing, gas optimization analysis, fuzzing for edge cases, and simulating real-world mainnet interactions via forking. Ideal for ensuring the security and reliability of decentralized applications (DApps).

This skill manages complex, large-scale refactoring operations that span hundreds or thousands of files. It provides phased execution with checkpoints, comprehensive rollback capabilities, and AI-assisted planning. Use it for codebase migrations, major architecture refactoring, or system-wide updates.

This skill acts as a Senior Solutions Architect and Tech Consultant for AI projects. It diagnoses a workspace—determining if it is a pure new project or an evolving system. It analyzes the existing stack and pain points to chart the optimal technical roadmap, generating detailed backlogs and implementation plans to ensure scalable, high-quality development.

Guides use of the Adaptyv Bio Foundry API and Python SDK to design protein experiments, submit sequences, automate workflows, and fetch BLI/SPR or thermostability assay results via authenticated requests for lab-ready studies.

This skill provides a comprehensive framework for performing end-to-end UI testing and verification on Android emulators. It guides users through systematic processes including device calibration, UI element discovery using uiautomator, performing complex interactions (tap, swipe, input), and robust reporting via visual screenshots and logcat analysis. Ideal for ensuring the stability and functional correctness of React Native or native Android applications.

Acts as a specialized System Engineer responsible for designing, drafting, and deploying new functional skills into a repository. It ensures that all new skills strictly adhere to the Diamond Standard and follow rigorous deployment protocols, generating necessary documentation (READMEs) and registry entries.

This comprehensive guide details the process of setting up Tailwind CSS v4 within an Expo React Native project. It ensures universal styling compatibility across iOS, Android, and Web by utilizing react-native-css and NativeWind v5. The setup covers required configurations for Metro, PostCSS, global CSS, and even provides reusable styled components for optimal development experience.

This skill guide details the integration of Jetpack Compose Views and modifiers into Expo applications. It enables developers to build high-fidelity, Android-native UI experiences by mirroring Jetpack Compose and Material Design 3 patterns within the Expo SDK environment. It provides instructions on using core components like Host, LazyColumn, and various modifiers for advanced Android UI construction.

This skill guides the comprehensive packaging and deployment of applications built with Makepad. It covers building for multiple target platforms, including desktop (Windows, macOS, Linux), mobile (Android, iOS), and WebAssembly. Use this guide when setting up CI/CD pipelines, generating distributable installers (.deb, .dmg, .apk, .ipa), or automating release artifacts using tools like cargo-packager and cargo-makepad.

This skill provides expert guidance on Makepad's cross-platform development lifecycle. It helps users understand platform-specific APIs, manage conditional compilation (e.g., macOS, Windows, WebGL2), and handle OS-specific behaviors across desktop, mobile, and web environments. Use it when targeting diverse deployment targets or integrating platform-level features.

A set of behavioral guidelines derived from Andrej Karpathy's observations to help users writing, reviewing, or refactoring code with Large Language Models (LLMs). It guides developers to avoid overcomplication, make surgical changes, explicitly surface assumptions, and define verifiable success criteria, promoting clean, simple, and robust code development.

This skill guides the development of EarLLM One, a complex Android application. The app captures real-time audio from Bluetooth earbuds, transcribes the voice using STT, sends the text input to a Large Language Model (LLM) via a robust pipeline, and plays the response back using TTS. It requires deep knowledge of Kotlin, Android development, Bluetooth profiles (A2DP/HFP), and advanced audio routing.