Skill UI

This skill provides advanced engineering patterns to dramatically improve the performance and reliability of Customer.io API integrations. It implements critical optimizations such as HTTP connection pooling, LRU deduplication caching, parallel batch processing, and fire-and-forget async tracking. Use this when integrating Customer.io into high-throughput, mission-critical systems to reduce latency from seconds to milliseconds.

Daily News Report orchestrates agents to scrape a curated URL list, filter high-quality technical findings, and compile them into daily Markdown summaries while monitoring batch progress and leveraging smart caching for reliability.

Automates database schema design guidance for backend projects, activating whenever you ask about schema patterns, best practices, or implementation in node.js, Python, Go, microservices, caching, or messaging contexts.

Guidelines for building scalable, maintainable Django apps covering REST API design with DRF, ORM best practices, caching, signals, middleware, and deployment-ready project layouts and settings.

Provides advanced patterns to significantly boost the performance and scalability of Documenso integrations. Key techniques covered include using templates to drastically reduce API round-trips, implementing metadata caching (e.g., using Redis) to minimize database lookups, and utilizing batch processing with concurrency control (p-queue) or asynchronous job queues (Bull) for handling large volumes of documents efficiently.

An expert guide for designing, building, and optimizing high-performance, enterprise-grade backend systems using the .NET ecosystem. Covers advanced C# features, ASP.NET Core microservices, data access patterns (EF Core/Dapper), Clean Architecture, DDD, and crucial performance considerations like caching and optimization. Ideal for architects needing to ensure scalable, maintainable, and testable production-ready APIs.

This skill guides the development of production-grade APIs, MCP servers, and enterprise backends using modern C#/.NET best practices. It covers essential architectural components such as dependency injection (DI), advanced caching strategies (e.g., Redis), data access optimization with EF Core/Dapper, and implementing resilience patterns, ensuring robust and scalable software design.

Automates guidance for implementing error handler middleware in backend services, covering Node.js, Python, Go, microservices, databases, caching, messaging, and architecture best practices with production-ready code and validation.

A comprehensive guide to boosting Evernote API integration performance and scalability. Techniques covered include implementing response caching (using Redis), performing metadata-only queries to minimize data transfer, batching multiple operations, and optimizing client connection management. Essential for building high-performance, reliable applications.

A comprehensive guide providing production-ready architectural patterns for building scalable, maintainable, and reliable integrations with Evernote. This skill covers microservices design, multi-level caching strategies (e.g., Redis), robust synchronization processes using webhooks and message queues, and establishing data mirroring schemas for complex SaaS applications.

A comprehensive guide to optimizing costs when using the Exa API. This skill teaches techniques like implementing query-level caching (LRU/Redis), strategically selecting search types (keyword vs. neural), query deduplication for batch jobs, and setting up usage monitoring and budget alerts. Use this when managing Exa billing or reducing API expenditure in RAG applications.

This skill manages and refines search results obtained from Exa's neural search API. It provides comprehensive control over content extraction scope, allowing users to selectively retrieve metadata, highlights, full text, or structured summaries. Key features include implementing result caching with TTL, deduplicating citations, and crucial token budget management, ensuring the retrieved context fits within LLM context window limits for robust RAG pipelines.