Skill UI

Apify Actorization is a process that transforms existing, standalone software or scripts (like CLI tools) into standardized, reusable serverless applications called Actors. Actors are packaged as Docker images, accepting structured JSON inputs and producing structured JSON outputs. This skill guides developers on migrating projects to the Apify platform, ensuring compatibility, and enabling scalable, event-driven data processing pipelines.

A comprehensive guide and methodology for identifying, measuring, and fixing performance bottlenecks across the entire stack. This skill covers optimizing database queries (e.g., N+1 problems, missing indexes), improving API efficiency (caching, parallel processing), and enhancing frontend performance (memoization, code splitting, image optimization). It emphasizes the critical 'Measure First' principle to ensure all optimizations are data-driven and effective.

Process videos, audio, images, and documents on Transloadit’s cloud using 86+ robots to encode formats, generate thumbnails, resize/watermark, run OCR, speech-to-text, moderation, and chain multi-step pipelines for scalable media automation.

pydicom is a comprehensive Python library designed for reading, inspecting, writing, and transforming local DICOM datasets and associated pixel data. It supports complex metadata handling, various transfer syntaxes, and provides tools for bounded de-identification and preflight checks. It is essential for medical image processing pipelines that require robust handling of Protected Health Information (PHI).

A comprehensive guide for debugging complex Claude integration issues beyond basic error handling. It covers advanced patterns such as managing inconsistent outputs (temperature control), validating tool use calls, recovering from streaming connection drops, handling max_tokens truncation, and correctly processing image/vision inputs. Essential for building robust, production-grade AI applications with Anthropic's API.

This guide outlines production-ready patterns for integrating SerpApi into robust search services. It covers essential best practices, including defining typed result interfaces for type safety, implementing LRU caching to conserve API credits, and building multi-engine abstractions to handle various search types (web, news, images). It also demonstrates asynchronous search processing for non-blocking operations.