Skill UI

This runbook provides comprehensive, step-by-step procedures for managing critical incidents involving Evernote integrations. It covers diagnosing API outages, resolving persistent rate limits, handling authentication failures, and recovering from data synchronization issues. Use this guide during production troubleshooting or service outages to ensure rapid and systematic incident response, including circuit breaker activation and graceful degradation strategies.

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 best practices for securing Evernote API integrations. It covers essential topics like secure credential management using environment variables or vaults, implementing CSRF-protected OAuth flows, encrypting access tokens at rest using AES-256-GCM, rigorous input validation, and ensuring secure logging practices by redacting sensitive data. Use this when hardening your application against data breaches.

This guide details the implementation of real-time synchronization for Evernote notes. It utilizes Evernote webhooks to detect changes (creation, updates, deletions) and pairs this with the Sync API to reliably fetch the actual data chunks. The process covers setting up webhook endpoints, managing sync state (USN), ensuring idempotency, and establishing a robust polling fallback mechanism for continuous data synchronization.

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 and implementation pattern for enforcing granular Role-Based Access Control (RBAC) on the Exa search API in multi-tenant or enterprise environments. This skill demonstrates how to implement API key scoping, restrict search domains by role, enforce maximum result limits, and track daily usage to ensure robust data governance and security.

A comprehensive guide containing minimal working examples for all core Exa search operations. It demonstrates basic metadata search, deep content extraction (text, highlights, summary), finding semantically similar pages, and retrieving content for specified URLs. Ideal for developers starting a new Exa integration or learning advanced RAG patterns.

A comprehensive guide to selecting and implementing the optimal Firecrawl architecture pattern for various data ingestion needs. Learn to choose between On-Demand, Scheduled Crawl Pipelines, and Real-Time Ingestion Pipelines based on data volume, required latency, and complexity. This is essential for designing scalable, production-grade scraping infrastructure, including RAG and knowledge base applications.

This workflow provides the primary mechanism for converting complex, live websites into clean, LLM-ready markdown content. It supports single-page scraping using `scrapeUrl`, deep multi-page site crawling with `crawlUrl`, and scalable async crawling for large datasets. Ideal for building robust content ingestion pipelines and reliably extracting structured information from the web.

This advanced workflow executes sophisticated web data gathering tasks. It supports three primary functions: LLM-powered structured data extraction using custom JSON schemas or prompts; efficient batch scraping of multiple known URLs; and rapid site mapping to discover all reachable URLs on a domain. Use this when you require reliable, typed data structures rather than raw markdown content.

This utility collects comprehensive diagnostic data for troubleshooting Firecrawl issues. It automatically checks environment variables, verifies API connectivity, captures SDK versions, gathers recent error logs, and runs diagnostic scrapes. This is essential for generating complete, redacted support tickets when encountering persistent or hard-to-reproduce bugs.