data-extractor

The data-extractor skill is designed to transform unstructured documents into structured, machine-readable data. By leveraging a unified library capable of processing PDFs, Word documents, emails, HTML, and other formats, this skill addresses the challenge of extracting consistent data from diverse sources. Users often encounter difficulty when trying to analyze or process documents of varying formats, especially when tables, images, and metadata need to be preserved. The data-extractor skill streamlines this process, enabling reliable extraction of both textual and tabular information along with associated metadata, regardless of document type or source.

Key features of the data-extractor include support for multiple document types such as PDF, Word, Excel, PowerPoint, email, HTML, Markdown, plain text, and images. The skill integrates with the MCP server and provides specialized tools like text and table extraction from PDFs. Users can choose between different extraction strategies including automatic detection, high-resolution parsing, fast extraction, and OCR-only processing for scanned documents. Extracted elements are returned with rich metadata, element types, unique identifiers, and structural categorization, allowing for precise filtering and further programmatic processing. The skill is compatible with multiple AI models, ensuring flexibility and reliability across different environments.

Use cases for the data-extractor skill include automating document workflows, converting legacy files into structured datasets, parsing emails for content and attachments, and extracting tables from financial or research reports. It is particularly useful for data analysts, developers, and business teams who need to process large volumes of heterogeneous documents efficiently. By enabling consistent and accurate data extraction, the skill reduces manual effort, improves accuracy, and accelerates decision-making processes in environments where structured data from unstructured sources is critical.