Skill UI

Expert proficiency in modern Angular development (v20+). Mastery includes Signals for fine-grained reactivity, Standalone Components for modular architecture, and Zoneless applications for peak performance. Skilled in implementing SSR, Hydration, and adopting the latest reactive patterns to build highly performant, scalable enterprise frontend applications.

Expert proficiency in modern Angular development (v20+). Mastery includes Signals for fine-grained reactivity, Standalone Components for modular architecture, and Zoneless applications for peak performance. Skilled in implementing SSR, Hydration, and adopting the latest reactive patterns to build highly performant, scalable enterprise frontend applications.

A specialist in modern Angular development (v17+), focusing on standalone components, Signals, and enterprise-grade architecture. This skill covers designing complex application flows, implementing robust state management using NgRx, managing reactive patterns with RxJS, optimizing bundle performance, and ensuring high code quality through rigorous testing and adherence to best practices.

An expert framework for creating realistic API mock services. It simulates real API behavior and contracts, enabling developers to build and test frontend components or integration workflows in parallel, even before the actual backend service is complete. Ideal for testing, demo environments, and validating API contracts.

Provides the Azure Resource Manager SDK for .NET to manage Application Insights resources. It enables comprehensive observability, allowing developers to programmatically create, configure, and manage performance monitoring components, API keys, and availability tests (WebTests) within Azure cloud environments.

Specializing in building high-performance, cloud-native applications using C# and the latest .NET 8+ framework. This skill covers end-to-end development, including building robust RESTful APIs (Minimal/Controller), implementing data access with Entity Framework Core, structuring code using CQRS/MediatR, and scaffolding Blazor components. Focuses on modern best practices like async/await, DTO mapping, and proper error handling.

A comprehensive guide to structuring effective custom prompts for AI coding assistants like Cursor. Learn the four components of prompt anatomy (Context, Task, Constraints, Format), and utilize reusable templates for complex tasks such as feature implementation, bug fixing, code review, and test generation. Master advanced techniques like Chain of Thought and Few-Shot examples to ensure high-quality, consistent code output.

This expert specializes in scaffolding complete, production-ready, accessible, and performant React and React Native components. It generates full boilerplate code including TypeScript definitions, unit tests, styles (CSS Modules, Styled-Components, Tailwind), and accessibility hooks, ensuring adherence to modern best practices for scalable frontend architecture.

This comprehensive workflow guides the entire lifecycle of building a complex full-stack feature, from initial architectural design to final integration testing. It enforces an API-first approach, ensuring that the database schema, backend services, and frontend components are developed cohesively, promoting scalability, testability, and adherence to modern industry best practices.

This skill coordinates the complex parallel execution of end-to-end, integration, and unit tests across multiple frameworks (e.g., Jest, Playwright, pytest) and CI/CD environments. It manages test splitting, worker allocation, tiered execution strategies, and aggregates results into unified reports. Ideal for optimizing the testing lifecycle and ensuring robust quality assurance in large-scale, multi-component applications. Key functions include intelligent retry logic for flaky tests and building CI pipelines (e.g., GitHub Actions matrix).

Runs automated visual regression tests via Percy, Chromatic, or BackstopJS to capture component or page screenshots, compare them against baselines, and flag unintended UI changes during feature updates or CI/CD runs.

Pyvene is a declarative framework designed for performing causal interventions on PyTorch neural networks. It allows researchers to conduct advanced experiments such as activation patching, causal tracing (ROME-style), and interchange intervention training (IIT). Use this library when you need to test causal hypotheses about model behavior, deeply interpret model components, or ensure reproducibility in advanced AI research.