Skill UI

This library provides a comprehensive Java client for interacting with Azure Table Storage and Cosmos DB Table API. It supports all standard CRUD operations (Create, Read, Update, Delete), advanced querying using OData filters, and batch transactions. Ideal for building scalable, high-performance applications requiring structured NoSQL data persistence on Azure Cloud.

This skill specializes in building robust, scalable, and cloud-native backend microservices using the latest .NET 8 and C# 12 features. It masters modern architectural patterns, including Clean Architecture, CQRS with MediatR, and minimal APIs. Expertise covers secure authentication (JWT), data persistence with Entity Framework Core, asynchronous programming best practices, and comprehensive unit/integration testing, ensuring highly maintainable and performant code.

Guide for designing, implementing, and scaling event stores in event-sourced systems, covering requirements, technology comparisons, PostgreSQL schemas, and Python persistence patterns.

A comprehensive skill designed to manage, track, and persist image assets generated by Ideogram's API. This tool addresses the critical issue of expiring image URLs by implementing a complete lifecycle pipeline. It handles metadata tracking (prompt, seed, model, style, etc.), immediate downloading, and robust storage to both local filesystems and cloud solutions like S3. Features include asset persistence, generation history logging, and image reproduction using stored seed values.

A comprehensive checklist and guide for deploying and maintaining Ideogram image generation services in a live production environment. It covers essential steps including API key management, request handling best practices, ensuring image persistence in durable storage, comprehensive error handling (401, 422, 429), and defining robust rollback procedures.

Specialized skill for designing, building, and optimizing modern, cloud-native enterprise Java applications. Covers microservices architecture, Spring Boot 3.x, reactive programming (WebFlux), advanced data persistence (JPA optimization, R2DBC), and robust security implementation (OAuth2/JWT). Use this when implementing DDD patterns, ensuring high test coverage, and managing complex transaction boundaries.

This guide documents common mistakes, gotchas, and anti-patterns encountered during Kling AI API integration. It covers critical best practices regarding data types, token management, polling, URL persistence, and prompt engineering to ensure stable and reliable AI video generation workflows. Essential reading for robust development and troubleshooting.

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.

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 developers migrating and upgrading Obsidian plugins across major API versions. It covers critical steps such as auditing plugin compatibility, implementing version-aware settings migration logic (for user data persistence), replacing deprecated API calls (e.g., Vault, Editor, FileManager), and updating configuration files like manifest.json to ensure stability on new Obsidian releases.

This guide identifies and explains common anti-patterns and critical pitfalls encountered when building applications on the Replit platform. Topics include data persistence, secret management, networking bindings, and environment configuration. Use this resource when conducting code reviews, onboarding new developers, or auditing existing Replit applications for best practices.

This pattern demonstrates how to build a robust, thread-safe data persistence layer in Swift using the 'actor' model. It combines fast in-memory caching with reliable file-backed storage, automatically eliminating data races and the need for manual locks. Ideal for offline-first applications and managing shared mutable state in modern Swift concurrency.