pdf

name	pdf
description	Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.
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.

Python Environment Setup

Always use a virtual environment if one is available. Before running any Python commands or installing packages, check for a venv in the workspace root.

Detecting and Using a Venv (PowerShell / Windows)

# Check for a venv and set $python accordingly
if (Test-Path ".venv\Scripts\python.exe") {
    $python = ".venv\Scripts\python.exe"
    $pip    = ".venv\Scripts\pip.exe"
} elseif (Test-Path "venv\Scripts\python.exe") {
    $python = "venv\Scripts\python.exe"
    $pip    = "venv\Scripts\pip.exe"
} else {
    $python = "python"
    $pip    = "pip"
}

# Install packages into the venv
& $pip install pdfplumber pypdf -q

# Run a script inside the venv
& $python my_script.py

Detecting and Using a Venv (bash / macOS / Linux)

if [ -f ".venv/bin/python" ]; then
    PYTHON=".venv/bin/python"
    PIP=".venv/bin/pip"
elif [ -f "venv/bin/python" ]; then
    PYTHON="venv/bin/python"
    PIP="venv/bin/pip"
else
    PYTHON="python3"
    PIP="pip3"
fi

$PIP install pdfplumber pypdf -q
$PYTHON my_script.py

Creating a Venv (if none exists and packages are missing)

python -m venv .venv
.venv\Scripts\pip install pdfplumber pypdf -q

Rule: Never install packages into the global Python environment when a venv is present. Always install into the venv.

Quick Start

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

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

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

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

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

import pdfplumber

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

Extract Tables

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

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

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

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)

Subscripts and Superscripts

IMPORTANT: Never use Unicode subscript/superscript characters (₀₁₂₃₄₅₆₇₈₉, ⁰¹²³⁴⁵⁶⁷⁸⁹) in ReportLab PDFs. The built-in fonts do not include these glyphs, causing them to render as solid black boxes.

Instead, use ReportLab's XML markup tags in Paragraph objects:

from reportlab.platypus import Paragraph
from reportlab.lib.styles import getSampleStyleSheet

styles = getSampleStyleSheet()

# Subscripts: use <sub> tag
chemical = Paragraph("H<sub>2</sub>O", styles['Normal'])

# Superscripts: use <super> tag
squared = Paragraph("x<super>2</super> + y<super>2</super>", styles['Normal'])

For canvas-drawn text (not Paragraph objects), manually adjust font the size and position rather than using Unicode subscripts/superscripts.

Command-Line Tools

pdftotext (poppler-utils)

# 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

# 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)

# 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

# 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

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

# 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

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

Extraction Caching

Always cache extracted content next to the source PDF. Before extracting anything from a PDF, check for a companion .extracted.json file. If it exists, load from it instead of re-extracting. After a fresh extraction, save the result to this cache file.

The companion file is named <pdf_stem>.extracted.json and lives in the same directory as the PDF.

Cache-Aware Extraction Pattern

import json
from pathlib import Path
import pdfplumber

def load_or_extract(pdf_path: str) -> dict:
    pdf_path = Path(pdf_path)
    cache_path = pdf_path.with_suffix(".extracted.json")

    # Return cached data if available
    if cache_path.exists():
        with cache_path.open("r", encoding="utf-8") as f:
            return json.load(f)

    # Extract fresh
    data = {"text": [], "tables": [], "metadata": {}}

    with pdfplumber.open(pdf_path) as pdf:
        for page in pdf.pages:
            text = page.extract_text() or ""
            tables = page.extract_tables() or []
            data["text"].append(text)
            data["tables"].append(tables)

    from pypdf import PdfReader
    reader = PdfReader(pdf_path)
    meta = reader.metadata or {}
    data["metadata"] = {
        "title": meta.get("/Title", ""),
        "author": meta.get("/Author", ""),
        "pages": len(reader.pages),
    }

    # Save cache
    with cache_path.open("w", encoding="utf-8") as f:
        json.dump(data, f, ensure_ascii=False, indent=2)

    return data

Rules

• Always check for <name>.extracted.json before extracting.
• Always save the cache file after a fresh extraction.
• The cache stores: text (list of strings per page), tables (list of tables per page), and metadata.
• If only a subset (e.g. text only) was originally cached and more is now needed, re-extract and overwrite the cache with the full result.
• The cache file is a plain JSON file — never binary. Keep it human-readable with indent=2.

Quick Helpers

# Get full text from cache or PDF
def get_text(pdf_path: str) -> str:
    data = load_or_extract(pdf_path)
    return "\n\n".join(data["text"])

# Get all tables from cache or PDF
def get_tables(pdf_path: str) -> list:
    data = load_or_extract(pdf_path)
    return [table for page_tables in data["tables"] for table in page_tables]

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