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.

Comprehensive guide for deploying Evernote integrations to robust production environments across major cloud platforms (AWS, GCP, Kubernetes). It covers advanced practices including multi-stage Docker builds, secure secrets management via Secret Manager, serverless function setup (Lambda), and configuring necessary health checks and CI/CD pipelines. Ideal for transitioning from testing to live deployment.

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 implementing Exa neural search at various scales. This resource details three distinct architectures—Direct Search, Cached Search, and full RAG Pipeline—allowing developers to choose the optimal blueprint based on traffic volume, required latency, and complexity. Use this when designing scalable Exa integrations, from simple UI search to advanced AI-powered research agents.

This guide details setting up robust Continuous Integration and Continuous Deployment (CI/CD) pipelines for Exa integrations. It covers unit tests, live integration testing using GitHub Actions secrets, and health checks. Use this when automating testing workflows or ensuring Exa API connectivity during builds and release gates.

A comprehensive workflow for executing advanced, semantic web searches using Exa. This tool moves beyond simple keyword matching, enabling deep context retrieval by understanding the intent behind the query. Key features include RAG context retrieval, content extraction (summaries, highlights, full text), and robust filtering by date range, specific domains, and content categories. Ideal for building knowledge base search features and complex research pipelines.

This advanced workflow extends beyond basic search capabilities. It allows users to discover pages semantically similar to a given URL, retrieve detailed content, highlights, and summaries from known URLs (batch processing), and generate comprehensive, cited answers using AI. Ideal for deep academic research, competitive intelligence, and content enrichment pipelines.

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.

A comprehensive guide for migrating search pipelines from traditional keyword-based APIs (Google, Bing, Tavily) to Exa's advanced neural search capabilities. This guide covers building an adapter layer, implementing feature-flag traffic shifting, translating legacy query syntax, and leveraging Exa's unique features like semantic and similarity search for superior results.

This skill provides the production-ready reference architecture for integrating Exa into complex applications. It covers advanced search pipelines, RAG implementation for context generation, and structured content discovery. Use this to design new Exa integrations, review project structures, or establish industry-standard best practices for neural search systems.

Build robust, event-driven integrations with Exa using scheduled monitoring and content alerts. This skill allows users to establish competitive intelligence pipelines, track content changes over time, and automate research digests. It supports new article detection, similarity monitoring, and reliable webhook delivery via background queues.

This skill provides guidance and patterns for generating structured JSON files that define complex workflow pipelines. It is essential for chaining multiple distinct AI models together, enabling multi-step, automated data processing. Use this when a single task requires a sequence of interconnected AI operations or services.