Skill UI

PDFtk Server is a powerful command-line tool designed for comprehensive manipulation and processing of PDF documents. It allows users to programmatically merge, split, rotate, secure (encrypt/decrypt), fill forms, apply watermarks, extract metadata, repair corrupted files, and much more. Ideal for automation scripts, batch processing, and any scenario requiring robust, scripted PDF handling from the terminal.

This lightweight, dependency-free script integrates with Gmail via standalone OAuth authentication, requiring no external server. It allows users to perform comprehensive operations—including searching, reading content, sending emails (with CC/BCC/HTML support), and modifying labels—directly from the command line. It is ideal for automating complex inbox workflows and batch processing tasks for Google Workspace accounts.

Pythonic wrapper around RDKit for drug discovery workflows: SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, and parallel batch processing. Returns native RDKit Mol objects.

Modal is a serverless cloud platform for running Python code on demand, including GPU-accelerated AI/ML workloads. Use for deploying models, serving web APIs, batch processing, and scaling Python applications to cloud containers with automatic scaling from zero to thousands of instances. Supports custom container images, persistent storage, and sub-second cold starts for low-latency inference.

A guide detailing four validated architectural patterns for integrating Claude APIs, catering to diverse scale and use cases. Choose between Serverless, Streaming Microservice, Queue-Based Pipelines, or complex Orchestrators to optimize performance, cost, and latency for your application, whether it's a real-time chatbot or large-scale batch processing.

Master building robust AI workflows with Anthropic's core APIs. This guide details two powerful patterns: real-time streaming via SSE events for interactive chat UIs, and the cost-efficient Message Batches API for asynchronously processing up to 100,000 requests. Includes comprehensive code examples for Python and TypeScript.

This guide teaches developers how to implement robust, event-driven architecture patterns with the Anthropic Claude API. It covers two key methods: Server-Sent Events (SSE) for real-time streaming responses, and the Message Batches API for high-volume, asynchronous bulk processing. Use this when building real-time chat features or processing large datasets offline.

A comprehensive guide for scaling Claude usage in high-throughput, enterprise-level applications. Learn four advanced strategies, including Message Batches for bulk processing, using p-limit for concurrency control, managing rate limit tier upgrades, and selecting the optimal model (e.g., Haiku) to balance speed and quality.

This module provides robust handling for Fathom API rate limits (60 requests/minute per user). It implements advanced retry strategies (handling 429 errors and server failures) and efficient batch processing to reliably sync large volumes of meeting intelligence, such as transcripts and action items, into external systems like CRMs or project trackers.

This document provides a comprehensive guide to managing rate limits when interacting with the Lucidchart API. It details specific limits for various operations (e.g., shape creation, document listing, exporting) and offers implementation patterns, including a token bucket rate limiter class and robust retry strategies. Use this knowledge to build automated workflows that reliably handle high-volume batch processing and prevent API throttling.

A comprehensive workflow for building NFT collection explorers and interacting with smart contracts using Alchemy's robust APIs. It covers fetching detailed NFT metadata (including OpenSea data and floor price), batch processing metadata, reading standard contract states (like ERC-20 balances), and verifying NFT ownership. Ideal for developing advanced Web3 applications, marketplaces, and data dashboards.

This guide addresses the performance degradation when automating Apple Notes, especially with large note collections (10,000+). It details architectural patterns, caching strategies, and scripting best practices (like batch processing and incremental sync) to prevent bottlenecks caused by excessive IPC calls, ensuring automation remains responsive at scale.