Overview

pdf-handling converts PDFs into readable text and images by running dedicated extraction scripts. It ensures you work with .txt or _unified.md outputs rather than raw PDFs, simplifying downstream processing. This standardizes input for NLP pipelines and documentation.

How This Skill Works

Use either Standard or Unified extraction scripts to convert a PDF into text and image assets. The Standard script is pdf_extract.py; the Unified script is pdf_extract_unified.py. After extraction, read the resulting .txt or _unified.md files, not the original PDF.

When to Use It

• You need textual content from a PDF for analysis or summarization.
• You want a consistent input format (.txt or _unified.md) for your pipeline.
• You need to extract embedded images alongside text for context.
• Preparing literature reviews or knowledge bases from PDFs.
• Avoid processing raw PDFs directly in your agent workflow.

Quick Start

1. Step 1: Run the appropriate extractor on your PDF (Standard or Unified).
2. Step 2: Read the produced .txt or _unified.md file, not the PDF.
3. Step 3: Use the extracted text/images for further processing.

Best Practices

• Choose the correct script (Standard or Unified) for your workflow.
• Verify extracted text encoding and review _unified.md for structure.
• Always read the generated .txt or _unified.md instead of the PDF.
• Keep a reproducible extraction step with the PDF source and output.
• Check for any extraction errors and re-run if needed.

Example Use Cases

• A researcher converts a batch of journal PDFs to .txt for NLP analysis.
• A team processes monthly financial reports into _unified.md for dashboards.
• A knowledge-base tool ingests product manuals via extracted text.
• An academic archive stores PDFs alongside their text transcripts.
• A content team summarizes e-books by extracting text for topic modeling.

Frequently Asked Questions