Skill UI

This skill helps manage and optimize resource usage and API costs within Evernote integrations. It provides utilities for monitoring monthly upload quotas, compressing images, resizing large files, and performing storage cleanup to prevent exceeding account limits and ensuring efficient data handling.

This skill details best practices for handling and structuring Evernote data. It covers designing local database schemas (SQL), advanced ENML content processing (to Markdown/HTML), managing attachments, and implementing reliable incremental synchronization using Update Sequence Numbers (USN). Ideal for developers building robust applications requiring deep, local data access and integrity checks for Evernote content.

Implement robust Role-Based Access Control (RBAC) specifically for Evernote integrations. This guide covers multi-tenant architectures, handling Evernote Business accounts, defining granular permissions (READ, WRITE, FULL), and creating authorization middleware. Essential for building secure, enterprise-grade SaaS applications that manage shared corporate data.

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 and toolkit for managing complex, large-scale data migration to and from Evernote. It covers essential processes including exporting notes in ENEX, Markdown, and JSON formats, importing structured data via ENML, and implementing robust migration runners with progress tracking and failure checkpointing. Ideal for developers handling massive data archives or cross-platform system integrations.

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 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 pipeline for processing, validating, and optimizing scraped web content from Firecrawl. This tool handles markdown cleaning, structured data extraction using Zod validation, content deduplication, and chunking tailored for Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. It ensures crawled data is standardized, clean, and ready for downstream consumption or knowledge base indexing.

A comprehensive guide for migrating web scraping pipelines from tools like Puppeteer, Playwright, and Cheerio to Firecrawl. This skill demonstrates how to replace complex, browser-intensive code with simple API calls, handling single-page scraping, structured data extraction using LLMs, and large-scale site crawling, while eliminating the need for manual browser management and anti-bot workarounds.

Master the implementation of Firecrawl webhooks for robust, real-time handling of asynchronous crawling and batch scraping jobs. Instead of inefficiently polling the crawl status, configure webhook endpoints to receive instant POST events (e.g., `crawl.page`, `crawl.completed`). This guide covers event types, signature verification using HMAC-SHA256, and processing streamed data for building advanced data pipelines.

This skill manages the complete lifecycle of Fireflies.ai meeting transcripts. It allows users to fetch raw data, export in various formats (JSON, CSV, SRT), sanitize sensitive personal information (PII Redaction), and enforce data retention policies to ensure compliance with regulations like GDPR and CCPA. Ideal for data governance and compliance workflows.

This resource provides production-ready, type-safe GraphQL client patterns for integrating with the Fireflies.ai API in both TypeScript and Python. It addresses critical engineering concerns such as robust error handling, managing singletons, implementing multi-tenant support, and ensuring structured data validation using best practices.