Skill UI

This SDK provides programmatic access to Azure AI Content Understanding, enabling the extraction of structured and semantic content from diverse multimodal sources. It supports analyzing documents (PDF, etc.), images, audio, and video files. Users can leverage prebuilt analyzers (like invoice or document search) or create custom analyzers with specific field schemas for advanced RAG and automated workflow implementations.

This SDK provides programmatic access to Azure AI Content Understanding, enabling the extraction of structured and semantic content from diverse multimodal sources. It supports analyzing documents (PDF, etc.), images, audio, and video files. Users can leverage prebuilt analyzers (like invoice or document search) or create custom analyzers with specific field schemas for advanced RAG and automated workflow implementations.

A comprehensive TypeScript client for the Azure Document Intelligence REST API. This SDK allows developers to programmatically extract structured data, text, and tables from various document types, including PDFs and images. It supports using prebuilt models (like invoices, receipts, and IDs) and building custom models for specialized document processing workflows.

A comprehensive TypeScript client for the Azure Document Intelligence REST API. This SDK allows developers to programmatically extract structured data, text, and tables from various document types, including PDFs and images. It supports using prebuilt models (like invoices, receipts, and IDs) and building custom models for specialized document processing workflows.

This SDK provides a comprehensive client library for Azure AI Document Translation service. It enables large-scale, batch translation of various document formats, including Word, PDF, Excel, and PowerPoint, while accurately preserving the original document structure and formatting. It supports features like glossary integration and async operations, making it ideal for enterprise content localization pipelines.

This SDK provides a comprehensive client library for Azure AI Document Translation service. It enables large-scale, batch translation of various document formats, including Word, PDF, Excel, and PowerPoint, while accurately preserving the original document structure and formatting. It supports features like glossary integration and async operations, making it ideal for enterprise content localization pipelines.

This comprehensive guide details the end-to-end process of building Retrieval-Augmented Generation (RAG) pipelines using LangChain. It covers critical steps: loading diverse document types (PDF, CSV, TXT), splitting text into manageable chunks, generating high-quality embeddings (OpenAI), storing them in various vector databases (FAISS, Pinecone), and finally constructing the full runnable chain for accurate and context-aware querying.

This skill provides an automated solution for transforming diverse, unstructured technical data sources—including documentation websites, GitHub repositories, and PDFs—into structured, actionable 'Claude AI skills.' It significantly streamlines the process of knowledge base creation, data structuring, and preparing large volumes of technical information for advanced AI applications.

Automates extracting and translating visa application documents from images into dual-language PDF outputs, handling HEIC conversion, auto-rotation, multi-engine OCR (Vision/EasyOCR/Tesseract), professional English translation, and structured PDF creation.

Python toolkit that queries the arXiv Atom API to search papers by keywords, authors, categories, or IDs, filter results, download PDFs, and emit structured JSON for AI/ML literature reviews and monitoring.

A comprehensive tool for searching, retrieving, and analyzing academic papers from the arXiv repository. Users can search by topic or specific ID to get metadata, download associated PDF files, and receive a structured summary of the paper's key contributions, making it essential for academic research and literature review.

Handles IMO/Putnam/USAMO/AIME style problems by generating refined proofs, then running a fresh-context adversarial verifier to catch flaws, finally producing honest confidence reports and verified PDFs when possible.