pdf-extractor_skill

Overview

This skill provides a compact toolkit to extract text and metadata from PDFs and to convert documents into plain text, Markdown, or HTML using Python-driven scripts. It is designed for integration into agent workflows and automation pipelines, delivering reliable parsing of pages, forms, and tables. The skill focuses on practical extraction outputs you can feed to downstream models or storage systems.

How this skill works

The skill exposes small scripts that open a PDF, read selected pages or the whole document, and return structured JSON with extracted text and metadata such as title, author, and page count. Conversion utilities transform PDF content into txt, md, or html formats. Parsing routines can locate and extract table data and form fields, returning them as structured objects ready for analysis or database ingestion.

When to use it

• Bulk-extract text from PDFs for indexing or search
• Convert reports or manuals to Markdown or HTML for publishing
• Parse tables and form fields into structured data for ETL workflows
• Preprocess documents before feeding into an LLM or agent
• Automate document ingestion in data pipelines

Best practices

• Specify pages when you only need parts of a document to speed up processing and reduce memory use
• Validate extracted metadata against expected values to catch parsing errors early
• Use the table/form parsing options for scanned reports that contain structured data
• Post-process converted Markdown/HTML to clean up layout artifacts from complex PDFs
• Run conversions in a sandbox or worker process for large batches to avoid blocking the main service

Example use cases

• Indexing a library of PDFs: extract text and metadata to populate a search index
• Data extraction from invoices: parse tables and form fields into CSVs for accounting
• Automated content conversion: turn reports into Markdown for a documentation site
• Preprocessing legal documents: extract relevant clauses and metadata before feeding to an LLM
• Agent-driven workflows: an agent calls the extract script, then summarizes results with an LLM