extract-pdf-content-and-metadata

name	Extract PDF Content and Metadata
description	Use this skill to extract the full text, title, abstract, and keywords from PDF files to determine their actual subject matter. Essential for content-based sorting when PDF filenames may be arXiv IDs or other non-descriptive identifiers.

When to use this skill

• When you need to read the actual content of a PDF paper to determine its subject
• When PDF filenames are identifiers (like 2105.03431v1.pdf) rather than descriptive titles
• Before sorting PDFs into subject folders to ensure accuracy

How to extract PDF content

Using PyPDF2 or pdfplumber

import pdfplumber

def extract_pdf_content(pdf_path):
    with pdfplumber.open(pdf_path) as pdf:
        # Extract first page (usually contains title and abstract)
        first_page = pdf.pages[0]
        text = first_page.extract_text()
        
        # Also extract from next 1-2 pages for full abstract
        if len(pdf.pages) > 1:
            text += "\n" + pdf.pages[1].extract_text()
        
        return text

Key information to extract

1. Title: Usually at the top of page 1
2. Abstract: Contains subject keywords and main topic
3. Keywords section: Often explicitly labeled
4. Introduction: First paragraph usually summarizes the field

What to look for in content

• LLM: Keywords like "language model", "transformer", "neural network", "NLP", "large language model", "BERT", "GPT"
• Trapped ion and quantum computing: Keywords like "trapped ion", "quantum computer", "qubit", "quantum gate", "quantum algorithm"
• Black hole: Keywords like "black hole", "event horizon", "gravitational", "spacetime", "Hawking", "relativity"
• DNA: Keywords like "DNA", "genetics", "gene", "molecular biology", "sequence", "protein"
• Music history: Keywords like "music", "composer", "symphony", "historical", "musical"

Store extracted content for analysis

Save the extracted text to a temporary variable or file for subject classification matching.