Skill UI

This skill provides a comprehensive guide to setting up isolated development, testing, and production vaults within Obsidian. It covers advanced techniques like symlinking plugin sources for hot reloading, implementing environment-specific configuration logic (e.g., managing debug modes and feature flags), and creating standardized vault templates for team onboarding, ensuring a robust and scalable development lifecycle.

This guide provides the best-practice reference architecture for developing complex Obsidian plugins. It details a highly modular project structure, enforcing separation of concerns by implementing dedicated layers for commands, views, and services (e.g., vault operations). Use this when designing a new plugin, reviewing existing project structure, or establishing standardized development practices for maintainable and scalable Obsidian extensions.

This guide provides essential, production-ready architectural patterns for building highly reliable Obsidian plugins. It addresses common development pitfalls such as managing typed settings with versioned migration, implementing safe vault operations (check-before-act), and handling event listeners to prevent memory leaks. Use these patterns when hardening your plugin for public release or refactoring for maximum stability.

A comprehensive guide covering essential security practices for developing Obsidian plugins. Learn how to safely handle sensitive credentials (avoiding data.json), implement robust input validation, prevent Cross-Site Scripting (XSS), secure URI handlers, and adhere to modern Electron and Obsidian guidelines for data privacy and system integrity.

This provides a local development loop for integrating the OpenEvidence clinical decision support API. Developers can build health-tech tools and quickly iterate using mock evidence queries, citation responses, and clinical summaries. It supports toggling between mock mode for rapid development and sandbox mode for validation against the real platform, ensuring development uses de-identified data.

This guide outlines the strict multi-environment setup for the OpenEvidence clinical AI system, crucial for maintaining HIPAA compliance. It details how to configure and validate separate environments (Development, Staging, Production) to manage different data classifications—synthetic, de-identified, and full PHI. The process emphasizes environment validation checks, BAA adherence, and robust audit logging to prevent regulatory violations and PHI exposure.

Use this tool to query, filter, and compare over 400 available AI models from OpenRouter. It allows users to check pricing, context lengths, model capabilities (e.g., tool calling), and select the best model based on specific needs, ensuring optimal performance and cost efficiency in AI development.

This skill automates the entire software development process overnight. By leveraging mandatory Git hooks, it strictly enforces Test-Driven Development (TDD). Use this when complex features need building, large codebases need refactoring, or bugs need fixing autonomously, guaranteeing that every commit passes rigorous testing cycles and meets high production-ready standards.

Guides API development teams through pagination helper tasks by providing best-practice guidance, ready-to-run code, and validation for REST, GraphQL, OpenAPI, and related authentication/design workflows.

This guide provides comprehensive principles for modern game development, covering engine selection (Unity, Unreal, Godot), platform integration (Steam features, console compliance), input abstraction, and performance optimization techniques. It helps developers make informed decisions when building cross-platform games for PC and console environments.

A comprehensive guide and implementation for setting up a fast, cost-effective local development loop for the Perplexity Sonar API. It includes generating type-safe client wrappers, utilizing fixtures for reliable mocking, and establishing a complete test suite with hot-reloading capabilities. This ensures stable development and manages high API call costs during iteration.

This guide details the process of migrating search and LLM functionalities from legacy APIs (Google CSE, Bing, SerpAPI) to Perplexity Sonar. It demonstrates how to utilize the Strangler Fig pattern to build a robust adapter layer. The primary benefit is consolidating a complex, multi-step pipeline (Search -> Raw Results -> LLM Summarization) into a single, efficient API call, ensuring automatic citation handling and streamlined development.