Skill UI

This skill provides a comprehensive guide to setting up Continuous Integration and Continuous Deployment (CI/CD) pipelines for applications integrating with Evernote. It covers advanced topics such as creating mock clients for deterministic unit testing, managing sandbox integration tests using GitHub Secrets, and structuring robust automated deployment workflows. Use this when ensuring code quality and reliable deployment for Evernote-based SaaS products.

A comprehensive toolkit designed to debug and troubleshoot Evernote API integrations. It provides deep logging of requests, responses, and timings. Features include an instrumented client wrapper for zero-config tracing, ENML content validation with auto-fix capabilities, and token inspection utilities to diagnose connectivity issues and quota limits.

This guide provides a comprehensive walkthrough for setting up the Evernote SDK and implementing OAuth 1.0a authentication. It details the entire process, from acquiring API keys via the developer portal, installing the SDK (for Node.js or Python), configuring environment variables, to initializing the client. Use this skill when starting a new Evernote integration or needing to verify secure API connectivity to the Evernote Cloud.

This guide details how to configure a robust multi-environment setup for Evernote integrations. It ensures proper isolation and configuration management between development, staging, and production environments. Learn to use environment variables, build an environment-aware client factory, and set up CI/CD pipelines, ensuring reliable and secure API connectivity across all stages.

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.

Provides comprehensive strategies and code examples for managing API rate limits when integrating with the Evernote platform. Techniques include implementing exponential backoff, wrapping clients with minimum delays, batching operations, and optimizing queries (e.g., using metadata only) to ensure robust, high-throughput, and stable API usage.

This guide provides comprehensive instructions for developers to install and set up the Exa SDK (exa-js for Node.js, exa-py for Python). It covers setting up API key authentication using environment variables and demonstrates client initialization for both major languages, enabling developers to utilize Exa's neural search API for web content retrieval.

This guide identifies and avoids anti-patterns and common integration mistakes when working with Exa's neural search API. It covers crucial topics such as distinguishing between keyword and neural searches, properly retrieving content, handling date filters, and managing client instances. Use this when reviewing Exa-powered code, onboarding new team members, or auditing existing integrations for robustness and correctness.

A comprehensive guide to implementing production-ready patterns for the Exa-js SDK. Covers critical architectural concerns such as client singletons, type-safe wrapper functions, advanced error handling, exponential backoff retry logic, and robust response validation using Zod. Use this when integrating Exa APIs into large-scale applications or establishing team coding standards for maximum stability and maintainability.

This guide provides production-ready patterns for integrating the Firecrawl SDK in TypeScript and Python. It covers essential architectural best practices, such as implementing singleton clients, typed service wrappers, automatic retries with exponential backoff, concurrency management, and robust response validation using Zod. Ideal for building scalable, reliable web scraping services.

A comprehensive guide for deploying Fireflies.ai integrations into production environments. This skill covers setting up the GraphQL client, implementing secure webhook receivers with signature verification, and managing multi-platform deployments across Vercel, Docker, and Google Cloud Run. Ideal for integrating AI-powered meetings data into production services.

This guide details a complete local development workflow for integrating with Fireflies.ai GraphQL. It outlines setting up the project structure, recording real API responses as mock fixtures, creating a custom mock client, and writing robust unit tests. Use this methodology to ensure fast, reliable, and isolated development cycles without dependency on live APIs or worrying about rate limits.