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PDFdocument processingPythondata extractionform fillingdocument generationfile manipulationAI agent
151.5k🕒 2026-06-09Source ↗

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npx skills add anthropics/skills

Works across Claude Code, Cursor, Codex, Copilot & Antigravity

The PDF skill allows programmatic manipulation of Portable Document Format files within Python environments. It provides mechanisms for reading document contents, extracting text data, managing metadata, and performing structural transformations such as merging or splitting files. Developers can interact with PDF content using pypdf for basic file handling or pdfplumber for high-precision text and table extraction. For generation tasks, the skill supports the reportlab library to construct structured documents programmatically. Beyond Python, the skill bridges to system-level utilities like qpdf, pdftk, and poppler-utils for efficient batch operations. This toolkit is suited for automating report generation, standardizing document workflows, converting unstructured PDFs into structured formats, and managing document pages without manual intervention.

When to Use This Skill

  • Converting monthly bank statements or invoices from PDF to Excel/CSV
  • Automating the assembly of multi-part reports from individual PDF snippets
  • Rotating or reordering pages in legacy documents via CLI scripts
  • Extracting plain text for downstream NLP or ingestion tasks
  • Generating dynamic operational reports from system data

How to Invoke This Skill

Example prompts that trigger this skill in Claude Code, Cursor, or Antigravity:

  • Extract the data from this table in my PDF
  • Merge these five PDF documents into a single file
  • Rotate the first two pages of this document 90 degrees
  • Convert this scanned PDF into searchable text
  • Generate a report PDF with these parameters
  • Split this PDF document into separate pages

Pro Tips

  • 💡For challenging text or table extraction from scanned PDFs, combine this skill with an OCR (Optical Character Recognition) tool to first convert images to searchable text.
  • 💡When dealing with sensitive information, always ensure proper handling of password-protected PDFs and consider encryption for newly generated documents.
  • 💡Optimize performance for large-scale PDF operations by processing documents in chunks and implementing robust error handling for corrupted files.

What this skill does

  • Extract text and visual layout information from digital PDFs
  • Parse structured tables into pandas DataFrames
  • Perform document transformations including rotating, splitting, and merging files
  • Generate custom PDF documents using canvas or platypus interfaces
  • Identify and extract document metadata like title and creator
  • Perform OCR on scanned PDFs by converting pages to images

When not to use it

  • Filling or programmatically modifying complex interactive PDF forms
  • High-fidelity visual editing or graphic design of existing PDF assets
  • Handling documents with heavy DRM or proprietary security wrappers

Example workflow

  1. Open the target PDF file using a library like pdfplumber
  2. Iterate through document pages to identify table regions
  3. Extract tabular data into a list or pandas DataFrame
  4. Apply formatting or cleaning to the extracted table data
  5. Export the clean data to a CSV or Excel file

Prerequisites

  • Python 3.x environment
  • System-level utilities like Poppler if using CLI tools
  • Tesseract-OCR for scanned document processing

Pitfalls & limitations

  • !Table extraction results can vary significantly based on document whitespace and borders
  • !Standard libraries struggle with encrypted or corrupted PDF files
  • !OCR-based extraction requires external dependencies like Tesseract and pdf2image
  • !PDF is a display format, not a data storage format; extraction is often heuristic

FAQ

Why is my text extraction returning garbage characters?
The PDF likely uses custom encoding or missing font maps, which prevents standard extraction tools from identifying character codes correctly.
Can this skill modify existing PDF forms?
No, this skill is focused on document structure and content extraction; modifying interactive form fields requires specialized libraries meant for PDF forms.
Which library should I use for table extraction?
Use pdfplumber for higher precision table detection and parsing, as it accounts for the layout of visual elements better than basic readers.

How it compares

This skill automates repetitive file operations that are error-prone when done manually, offering more control and repeatability than using general-purpose GUI-based PDF editors.

Source & trust

152k stars🕒 Updated 2026-06-09
📄 Full skill instructions — original source: anthropics/skills
# 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

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)


## 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 |

## 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

How to Use This Skill Unit

Option A: Project-Specific (Recommended)

  1. Click "Download" above
  2. In your project, create the directory: .agent/skills/pdf/
  3. Save the file as SKILL.md
  4. The agent will automatically discover the skill based on its description.

Option B: Global Installation (All Agents)

Save the file to these locations to make it available across all projects:

  • Claude Code: ~/.claude/skills/anthropics/skills/pdf/SKILL.md
  • Cursor: ~/.cursor/skills/anthropics/skills/pdf/SKILL.md
  • Antigravity: ~/.gemini/antigravity/skills/anthropics/skills/pdf/SKILL.md

🚀 Install with CLI:
npx skills add anthropics/skills

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Complete Guide

How to use this Skill in Claude Code & Cursor

For Claude Code (CLI)

To use this skill in Claude Code, copy the rule content into your project's custom instructions or follow our Add-Skill CLI guide. This ensures Claude follows your standards during every code generation.

For Cursor & Windsurf

For Cursor or Windsurf, individual skills are best used in the "Rules for AI" section. This specific unit helps the agent avoid python development issues, leading to cleaner, more efficient code.

Why the skill format matters: the standardized Agent Skills format lets your AI agent load detailed instructions only when they are relevant, keeping your prompt clean while improving results.

Source & attribution

This skill is categorized under Python Development and is published by Anthropic, maintained in anthropics/skills.

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