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pdf skill

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This skill helps you process PDFs by extracting text, merging, splitting, and filling forms to streamline document workflows.

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SKILL.md
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---
name: pdf
description: Extract text/tables, create, merge, split PDFs. Fill PDF forms programmatically. Use for PDF processing, generation, form filling, document analysis, batch operations.
license: Proprietary. LICENSE.txt has complete terms
---

# PDF Processing Guide

## Overview

This guide covers essential PDF processing operations using Python libraries and command-line tools. For advanced features, JavaScript libraries, and detailed examples, see reference.md. If you need to fill out a PDF form, read forms.md and follow its instructions.

## Quick Start

```python
from pypdf import PdfReader, PdfWriter

# Read a PDF
reader = PdfReader("document.pdf")
print(f"Pages: {len(reader.pages)}")

# Extract text
text = ""
for page in reader.pages:
    text += page.extract_text()
```

## Python Libraries

### pypdf - Basic Operations

#### Merge PDFs
```python
from pypdf import PdfWriter, PdfReader

writer = PdfWriter()
for pdf_file in ["doc1.pdf", "doc2.pdf", "doc3.pdf"]:
    reader = PdfReader(pdf_file)
    for page in reader.pages:
        writer.add_page(page)

with open("merged.pdf", "wb") as output:
    writer.write(output)
```

#### Split PDF
```python
reader = PdfReader("input.pdf")
for i, page in enumerate(reader.pages):
    writer = PdfWriter()
    writer.add_page(page)
    with open(f"page_{i+1}.pdf", "wb") as output:
        writer.write(output)
```

#### Extract Metadata
```python
reader = PdfReader("document.pdf")
meta = reader.metadata
print(f"Title: {meta.title}")
print(f"Author: {meta.author}")
print(f"Subject: {meta.subject}")
print(f"Creator: {meta.creator}")
```

#### Rotate Pages
```python
reader = PdfReader("input.pdf")
writer = PdfWriter()

page = reader.pages[0]
page.rotate(90)  # Rotate 90 degrees clockwise
writer.add_page(page)

with open("rotated.pdf", "wb") as output:
    writer.write(output)
```

### pdfplumber - Text and Table Extraction

#### Extract Text with Layout
```python
import pdfplumber

with pdfplumber.open("document.pdf") as pdf:
    for page in pdf.pages:
        text = page.extract_text()
        print(text)
```

#### Extract Tables
```python
with pdfplumber.open("document.pdf") as pdf:
    for i, page in enumerate(pdf.pages):
        tables = page.extract_tables()
        for j, table in enumerate(tables):
            print(f"Table {j+1} on page {i+1}:")
            for row in table:
                print(row)
```

#### Advanced Table Extraction
```python
import pandas as pd

with pdfplumber.open("document.pdf") as pdf:
    all_tables = []
    for page in pdf.pages:
        tables = page.extract_tables()
        for table in tables:
            if table:  # Check if table is not empty
                df = pd.DataFrame(table[1:], columns=table[0])
                all_tables.append(df)

# Combine all tables
if all_tables:
    combined_df = pd.concat(all_tables, ignore_index=True)
    combined_df.to_excel("extracted_tables.xlsx", index=False)
```

### reportlab - Create PDFs

#### Basic PDF Creation
```python
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas

c = canvas.Canvas("hello.pdf", pagesize=letter)
width, height = letter

# Add text
c.drawString(100, height - 100, "Hello World!")
c.drawString(100, height - 120, "This is a PDF created with reportlab")

# Add a line
c.line(100, height - 140, 400, height - 140)

# Save
c.save()
```

#### Create PDF with Multiple Pages
```python
from reportlab.lib.pagesizes import letter
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, PageBreak
from reportlab.lib.styles import getSampleStyleSheet

doc = SimpleDocTemplate("report.pdf", pagesize=letter)
styles = getSampleStyleSheet()
story = []

# Add content
title = Paragraph("Report Title", styles['Title'])
story.append(title)
story.append(Spacer(1, 12))

body = Paragraph("This is the body of the report. " * 20, styles['Normal'])
story.append(body)
story.append(PageBreak())

# Page 2
story.append(Paragraph("Page 2", styles['Heading1']))
story.append(Paragraph("Content for page 2", styles['Normal']))

# Build PDF
doc.build(story)
```

## Command-Line Tools

### pdftotext (poppler-utils)
```bash
# Extract text
pdftotext input.pdf output.txt

# Extract text preserving layout
pdftotext -layout input.pdf output.txt

# Extract specific pages
pdftotext -f 1 -l 5 input.pdf output.txt  # Pages 1-5
```

### qpdf
```bash
# Merge PDFs
qpdf --empty --pages file1.pdf file2.pdf -- merged.pdf

# Split pages
qpdf input.pdf --pages . 1-5 -- pages1-5.pdf
qpdf input.pdf --pages . 6-10 -- pages6-10.pdf

# Rotate pages
qpdf input.pdf output.pdf --rotate=+90:1  # Rotate page 1 by 90 degrees

# Remove password
qpdf --password=mypassword --decrypt encrypted.pdf decrypted.pdf
```

### pdftk (if available)
```bash
# Merge
pdftk file1.pdf file2.pdf cat output merged.pdf

# Split
pdftk input.pdf burst

# Rotate
pdftk input.pdf rotate 1east output rotated.pdf
```

## Common Tasks

### Extract Text from Scanned PDFs
```python
# Requires: pip install pytesseract pdf2image
import pytesseract
from pdf2image import convert_from_path

# Convert PDF to images
images = convert_from_path('scanned.pdf')

# OCR each page
text = ""
for i, image in enumerate(images):
    text += f"Page {i+1}:\n"
    text += pytesseract.image_to_string(image)
    text += "\n\n"

print(text)
```

### Add Watermark
```python
from pypdf import PdfReader, PdfWriter

# Create watermark (or load existing)
watermark = PdfReader("watermark.pdf").pages[0]

# Apply to all pages
reader = PdfReader("document.pdf")
writer = PdfWriter()

for page in reader.pages:
    page.merge_page(watermark)
    writer.add_page(page)

with open("watermarked.pdf", "wb") as output:
    writer.write(output)
```

### Extract Images
```bash
# Using pdfimages (poppler-utils)
pdfimages -j input.pdf output_prefix

# This extracts all images as output_prefix-000.jpg, output_prefix-001.jpg, etc.
```

### Password Protection
```python
from pypdf import PdfReader, PdfWriter

reader = PdfReader("input.pdf")
writer = PdfWriter()

for page in reader.pages:
    writer.add_page(page)

# Add password
writer.encrypt("userpassword", "ownerpassword")

with open("encrypted.pdf", "wb") as output:
    writer.write(output)
```

## Quick Reference

| Task | Best Tool | Command/Code |
|------|-----------|--------------|
| Merge PDFs | pypdf | `writer.add_page(page)` |
| Split PDFs | pypdf | One page per file |
| Extract text | pdfplumber | `page.extract_text()` |
| Extract tables | pdfplumber | `page.extract_tables()` |
| Create PDFs | reportlab | Canvas or Platypus |
| Command line merge | qpdf | `qpdf --empty --pages ...` |
| OCR scanned PDFs | pytesseract | Convert to image first |
| Fill PDF forms | pdf-lib or pypdf (see forms.md) | See forms.md |

## Next Steps

- For advanced pypdfium2 usage, see reference.md
- For JavaScript libraries (pdf-lib), see reference.md
- If you need to fill out a PDF form, follow the instructions in forms.md
- For troubleshooting guides, see reference.md

Overview

This skill provides a practical toolkit for PDF processing using Python and common CLI tools. It covers extracting text and tables, creating and composing PDFs, splitting and merging files, rotating and watermarking pages, and programmatic form filling. The goal is to streamline document automation, analysis, and batch operations with clear, ready-to-use patterns. Suitable for both quick scripts and production workflows.

How this skill works

The skill uses lightweight Python libraries—pypdf for reading/writing, pdfplumber for layout-aware text and table extraction, reportlab for PDF generation, and pytesseract/pdf2image for OCR on scanned documents. It also demonstrates command-line utilities (pdftotext, qpdf, pdfimages) for fast, scriptable operations. Example snippets show merging, splitting, metadata handling, rotating, watermarking, extracting images/tables, encrypting, and form filling patterns so you can integrate them into apps or pipelines.

When to use it

  • Batch converting many PDFs to text or Excel-ready tables for analysis
  • Generating reports or multi-page PDFs programmatically from templates
  • Merging, splitting, rotating, or watermarking documents before distribution
  • Extracting data from tables or images embedded in PDFs for ingestion
  • Filling PDF forms automatically in a workflow or application

Best practices

  • Prefer pdfplumber for layout-preserving text and table extraction, but fall back to OCR for scanned pages
  • Use pypdf for fast in-memory merge/split/rotate operations and to preserve metadata when possible
  • Validate extracted tables programmatically (headers, row counts) before downstream use
  • Use reportlab or Platypus for templated, repeatable PDF generation rather than drawing text at fixed coordinates
  • When handling many files, stream pages rather than loading all PDFs into memory to reduce peak RAM

Example use cases

  • Automate monthly report assembly by merging generated pages and stamping a watermark
  • Extract invoice line items from PDFs into Excel using pdfplumber + pandas
  • Split a large scanned contract into per-signature pages and run OCR to make it searchable
  • Fill customer onboarding PDF forms programmatically and produce password-protected copies
  • Batch-extract embedded images for archival or image-based analysis using pdfimages

FAQ

Which tool is best for extracting tables reliably?

Start with pdfplumber for structured PDFs; if tables are poorly detected, adjust table extraction settings or convert pages to images and use OCR-based table recovery.

How do I handle scanned PDFs?

Convert pages to images with pdf2image and run pytesseract for OCR, then post-process text into structured outputs.