pdf-extraction

The pdf-extraction skill is designed to extract structured and unstructured data from PDF documents using pdfplumber through MCP tools. It addresses the common challenge of working with PDFs, which often contain complex layouts that make it difficult to reliably retrieve text, tables, and document metadata. By leveraging dedicated extraction tools, this skill enables users to transform static PDF content into usable, machine-readable data for analysis, reporting, or downstream processing.

This skill provides capabilities for extracting text content, detecting and retrieving tables, and accessing document metadata such as title, author, and creation information. It uses pdfplumber’s advanced parsing features, including character-level positioning, word grouping, and layout-aware extraction, to ensure accuracy when working with structured documents. Through MCP integration, it exposes tools such as extract_text_from_pdf and extract_tables_from_pdf, allowing targeted extraction workflows depending on user needs. It is particularly useful for handling multi-page documents, preserving reading order, and extracting tabular data with minimal manual cleanup.

Common use cases include processing financial reports, extracting invoice data, converting PDF tables into structured formats like CSV or Excel, and mining text from research papers or business documents. It is intended for analysts, developers, and data engineers who regularly work with PDF-based data sources and need reliable programmatic extraction. The skill is especially valuable in automation pipelines where PDFs must be converted into structured datasets for analytics or storage systems.