Skill UI

This comprehensive guide outlines best practices for developing modern, scalable applications using the Next.js App Router. It covers critical architectural patterns, including the decision-making process between Server and Client Components, advanced data fetching strategies (caching, revalidation), file-system routing principles, API route handling, and performance optimizations like image and bundle splitting. Essential knowledge for building robust full-stack React applications.

A comprehensive guide to building modern, high-performance full-stack applications using Next.js 14+ and the App Router. Covers advanced concepts including React Server Components (RSC), Server Actions, optimized data fetching with caching, implementing robust SEO via generateMetadata, and best practices for deployment and performance tuning (Vercel).

Guide detailing Next.js 14+ App Router patterns, emphasizing Server Components, ISR caching, middleware, server actions, and parallel routes to build scalable, data-driven apps with minimal client JS.

Runs build, test, lint, serve, and other tasks in Nx workspaces via run, run-many, and affected flows, honoring the detected package manager and target metadata while exposing filters, parallelism, and caching controls for CI or local workflows.

Guides configuring, exploring, and optimizing Nx monorepos: list and inspect projects, fetch full project configs, run affected analyses, and tune caching or CI/CD workflows without executing tasks or generators.

A comprehensive guide to designing, configuring, and optimizing Nx monorepo workspaces. This skill covers best practices for project structure (apps, libs), dependency management, optimizing build caching, enforcing module boundaries, and setting up complex build pipelines for large-scale JavaScript/TypeScript applications.

A comprehensive guide for developers implementing robust data persistence and file handling within Obsidian plugins. It covers best practices for saving plugin configurations, reliable vault file I/O (read, write, append), safe manipulation of YAML frontmatter via metadata cache, and registering event listeners for file renames and deletions. Essential for building stable, data-intensive plugins.

A comprehensive guide for optimizing Obsidian plugin performance, especially when dealing with large knowledge vaults (10,000+ files). Learn advanced techniques such as profiling bottlenecks with DevTools, implementing lazy initialization to speed up startup, processing files in manageable batches with UI yielding, and utilizing LRU caches for bounded memory management. Essential for creating stable, high-performance plugins.

A comprehensive guide to the Obsidian event system, detailing how to handle various lifecycle and state changes within plugins. Learn to register listeners for vault events (file creation, modification, deletion), workspace events (layout changes, view activation), and metadata cache updates. Essential for building highly reactive and deeply integrated Obsidian plugins.

This skill provides comprehensive guidelines and optimization strategies for managing API usage and reducing costs when interacting with OpenEvidence. It covers best practices such as implementing caching, batching requests, selecting appropriate API tiers, and monitoring usage dashboards to ensure efficient and cost-effective data retrieval in healthcare applications.

This guide provides comprehensive strategies for optimizing API interactions with OpenEvidence. It covers implementing robust caching mechanisms (TTL management), efficient batch retrieval of citations, connection pooling, and handling rate limits. By applying these techniques, users can significantly reduce query latency, ensuring smooth system performance even when handling complex multi-condition queries or large citation batches.

This skill provides comprehensive strategies for implementing robust caching mechanisms for OpenRouter API calls. It covers in-memory caching using Python, persistent caching with Redis for scalability, and utilizing provider-specific features like Anthropic prompt caching. By implementing deterministic key generation and managing Time-To-Live (TTL), developers can significantly reduce API costs and improve application latency when handling repetitive or similar LLM queries, making it ideal for RAG systems and high-volume applications.