Skill UI

This comprehensive workflow guides the process of building and deploying new features that must maintain consistent functionality and design across multiple operating systems (web, mobile, and desktop). It strictly follows an API-first architecture, starting with defining shared API contracts (OpenAPI/GraphQL) and a unified design system. The process then guides parallel implementation across specialized stacks like React/Next.js, SwiftUI, and Jetpack Compose, ensuring high code consistency and faster time-to-market.

Mutation Test Runner delivers automated help for configuring and executing mutation testing workflows, offering best practices, code samples, and validation steps whenever mutation test runner patterns or automation requests appear.

Guides writing Python code inside n8n Code nodes, covering recommended use cases, helper data access patterns, Beta vs Native modes, and webhook/data handling rules so you can safely script workflow transformations with Python.

This elite workflow simulates the comprehensive process of a Senior Software Engineer and Product Designer. It enforces a rigorous development pipeline: starting with planning, solid architecture, and TDD implementation, through clean code practices, and technical auditing. Crucially, it includes mandatory, pixel-perfect UX visual validation to ensure the output is flawless, consistent, and production-grade.

This advanced workflow emulates an elite Senior Software Engineer and Senior Product Designer, ensuring maximum quality output. It enforces a rigorous, multi-step process including ultra-fast planning, solid architecture design, TDD implementation, clean code refactoring, technical auditing, and crucially, mandatory pixel-perfect UI/UX validation. Use this for complex projects demanding both robust code and flawless visual presentation.

Guides users through advanced network security scanning tasks by offering best-practice workflows, code/config generation, and validation for compliance, penetration testing, and enterprise security scenarios.

Guides developers through OAuth2 Flow Helper tasks within Security Fundamentals by providing best-practice instructions, generating production-ready code/configs, and validating outputs for authentication, validation, and secure coding.

Automates Open Graph metadata guidance for frontend workflows, offering best practices, React/Vue/CSS tips, and ready-to-apply code.

This guide provides comprehensive documentation and code examples for integrating OpenEvidence webhooks. Learn how to subscribe to real-time events (e.g., query completion, evidence updates, new citations) to keep clinical decision support systems fresh. Use these webhooks to trigger downstream audit workflows and ensure immediate data synchronization.

This skill provides comprehensive methods for implementing intelligent model routing using the OpenRouter API. It allows developers to dynamically select the optimal Large Language Model (LLM) based on specific criteria: task complexity (e.g., classification, code generation), required capability (e.g., deep reasoning vs. simple Q&A), cost budget, and latency tolerance. Implement cost-aware, task-based, and complexity-based routing to maximize system efficiency and minimize operational expenses when building multi-model applications.

OpenRouter provides a unified API gateway to access leading AI models (OpenAI, Anthropic, Google, Meta, etc.) from a single endpoint. This skill is essential for developers who need to compare model performance (latency, tokens) across different providers, build resilient cross-provider fallback workflows, or manage complex AI pipelines without writing provider-specific code. It supports feature normalization and BYOK (Bring Your Own Key) for high-volume production use.

This skill provides comprehensive best practices and Python code examples for optimizing API calls to Large Language Models (LLMs) via OpenRouter. It covers benchmarking model latency, implementing streaming to achieve low Time-To-First-Token (TTFT), and processing requests concurrently using asynchronous programming (asyncio). Use this when developing real-time, high-throughput applications that require predictable and fast model responses.