PDF Extractor

Extract text, tables, and images from PDF files using pdfplumber - turn static PDFs into usable data.

When to Use This Skill

• Report processing - Extract data from PDF reports
• Table extraction - Convert PDF tables to CSV
• Image collection - Pull images from presentations
• Text mining - Bulk convert PDFs to searchable text
• Research - Process academic papers and whitepapers

What Claude Does vs What You Decide

Dependencies

pip install pdfplumber pypdf click pandas
# For image extraction:
pip install Pillow

Commands

Extract Text

python scripts/main.py text document.pdf
python scripts/main.py text document.pdf --pages 1-5

Extract Tables

python scripts/main.py tables report.pdf --output tables.csv
python scripts/main.py tables financial.pdf --page 3

Extract Images

python scripts/main.py images presentation.pdf --output ./images/

Merge PDFs

python scripts/main.py merge doc1.pdf doc2.pdf --output combined.pdf

PDF Info

python scripts/main.py info document.pdf

Examples

Example 1: Extract Financial Tables

python scripts/main.py tables annual-report.pdf --output financials.csv

# Output: financials.csv with all tables found
# Also creates individual CSVs: table_page3_1.csv, table_page5_1.csv

Example 2: Batch Convert to Text

python scripts/main.py batch ./pdfs/ --output ./text/

# Converts all PDFs in folder to .txt files

Example 3: Extract Specific Pages

python scripts/main.py text whitepaper.pdf --pages 1,5-10,15

# Extracts only pages 1, 5-10, and 15

Skill Boundaries

What This Skill Does Well

• Structuring data analysis
• Identifying patterns and trends
• Creating visualization frameworks
• Calculating statistical measures

What This Skill Cannot Do

• Access your actual data
• Replace statistical expertise
• Make business decisions
• Guarantee prediction accuracy

Related Skills

• web-scraper - Scrape web content
• content-repurposer - Repurpose extracted content

Skill Metadata

• Mode: centaur

category: automation
subcategory: document-processing
dependencies: [pdfplumber, pypdf, pandas]
difficulty: beginner
time_saved: 4+ hours/week