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.

This skill generates Mermaid diagrams to create visual organizational tools specifically designed for neurodivergent individuals, particularly those with ADHD. It helps externalize executive functions, reducing cognitive overwhelm by breaking down complex tasks into compassionate, manageable micro-steps. The tool auto-detects signs of distress and supports multiple accessibility modes (e.g., colorblind-safe, monochrome), making it useful for project planning, decision mapping, and personal productivity tracking.

A comprehensive guide for developers to diagnose and fix the six most common errors encountered when building Obsidian plugins. It covers critical technical issues such as null pointer exceptions, manifest loading failures, incorrect CSS paths, command registration timing, and state persistence bugs, ensuring reliable and stable plugin functionality.

Master the art of building complex, production-grade user interfaces for Obsidian plugins. This guide covers implementing custom sidebar views (ItemView), interactive modal dialogs (for forms and confirmations), context menus, status bar widgets, and advanced editor commands. Learn how to interact with the Obsidian Vault API for file manipulation, significantly enhancing your plugin's functionality and user experience.

Automates the Continuous Integration and Continuous Deployment (CI/CD) process for OpenEvidence clinical decision support integrations. This setup ensures that unit tests (using mocked evidence queries and citations) and live API connectivity tests are run automatically on every pull request and merge to main. It validates critical functions like query formatting, evidence parsing, citation extraction, and response quality scoring, maintaining high reliability for medical AI applications.

This skill provides advanced strategies for managing large language model (LLM) context windows and token usage via OpenRouter. Functions include context window lookup, context-aware model selection, conversation trimming, document chunking for RAG, and prompt caching. Use this when dealing with long conversations or large documents to maximize quality while minimizing API costs and preventing context limit overflow.

Implements standardized function/tool calling across various large language models (LLMs) via OpenRouter. This unified approach allows developers to build sophisticated, multi-step agents and complex workflows, regardless of the underlying provider (e.g., GPT-4o, Claude 3.5). It simplifies the process of achieving structured output and tool-augmented reasoning in production systems.

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).

This guide provides a comprehensive overview of PDF manipulation techniques using both Python libraries (like pypdf, pdfplumber, reportlab) and command-line tools (like pdftotext, qpdf). It covers core functionalities such as merging, splitting, rotating, extracting text and structured tables, creating new PDFs, and performing OCR on scanned documents, making it essential for automation workflows.

This guide provides comprehensive instructions for deploying applications powered by the Perplexity Sonar API. It covers setup for major cloud platforms like Vercel, Google Cloud Run, and Docker. Users can deploy production-ready endpoints, manage required secrets, implement edge functions, and utilize caching mechanisms (e.g., Redis) to ensure high availability and performance in production environments.

Provides a minimal working example demonstrating how to integrate Perplexity's API for web-grounded answers. Learn to perform advanced searches, retrieve cited sources, and handle features like domain filtering and streaming using both TypeScript and Python. Ideal for integrating real-time, source-backed search functionality into applications.

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.