Skill UI

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.

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.

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.

A comprehensive guide for deploying Exa-powered search APIs to production environments. This skill covers best practices for integrating Exa into Vercel Edge Functions, Docker containers, and Google Cloud Run. It includes detailed steps for secret management, implementing Redis caching for performance, and setting up health check endpoints, ensuring a robust and scalable production search solution.

This guide details how to configure and manage the Exa search API across development, staging, and production environments. It ensures API key isolation, implements environment-specific search limits, and provides strategies for cost control and performance optimization by utilizing caching layers like Redis. Essential for scalable, multi-stage deployments.

This comprehensive guide details how to set up a robust, local development environment for Firecrawl integrations. It utilizes self-hosted Docker containers and Redis to eliminate reliance on paid API credits during development. The setup supports both unit testing (with mocked SDK) and full integration testing against the local instance, ensuring a seamless and efficient workflow for scraping and web data extraction projects.

A comprehensive guide detailing best practices for optimizing GraphQL queries against external APIs, demonstrated using Fireflies.ai. Learn advanced techniques such as requesting minimum required fields (field selection), implementing robust caching strategies (LRU/Redis), and managing rate limits through controlled batch processing and pagination. This ensures stable, fast, and scalable data retrieval.

Automates Happy Scribe workflows via Composio's Rube MCP, ensuring tools are rediscovered before each run and connections stay active for schema-compliant, multi-tool execution.

Implements multi-tier database caching using Redis, in-memory stores, and CDN integration to reduce load, lower query latency, and support scalable cache-aside, write-through, or read-through workflows when users ask for caching optimizations.

This skill provides a comprehensive pattern for building robust, scalable, and fault-tolerant asynchronous video generation pipelines using Kling AI. It implements advanced software engineering patterns, including Redis queues (Producer/Consumer), State Machines, and event-driven callbacks. It is designed for managing complex, multi-step AI workflows—such as generate, extend, and download—ensuring reliable background task processing.