---
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."
dependencies:
  commands:
    - python3
  tools:
    - skillScriptTool
    - skillFileTool
platforms:
  - macos
  - linux
  - windows
---

> **Important:** All `scripts/` paths are relative to this skill directory.
> Use `run_skill_script` tool to execute scripts, or run with: `cd {this_skill_dir} && python scripts/...`

# PDF Processing Guide

## Prerequisites

- **pypdf**: core PDF reading and writing
- **pdfplumber**: text and table extraction
- **reportlab**: PDF creation
- **pdftotext** (poppler-utils): command-line text extraction
- **pdftoppm** (poppler-utils): PDF-to-image conversion
- **qpdf**: PDF manipulation (merge, split, rotate, decrypt)

## Tool Selection Decision Table

Choose the right approach before starting:

| Input | Condition | Recommended Tool |
|-------|-----------|-----------------|
| URL | PDF accessible via URL | `web_extract(url)` — fastest, no download needed |
| Local file | Text-native PDF (generated by software) | `pymupdf` — ~25 MB install, instant extraction |
| Local file | Scanned/image-only PDF (no selectable text) | `marker-pdf` — OCR with layout preservation (~5 GB, needs GPU or CPU) |
| Local file | Form filling or page manipulation | `pypdf` / `pdfplumber` + form scripts |
| Local file | NLP editing or semantic search | `nano-pdf` — sentence-level operations |

**URL-first rule**: If the user provides a URL, always try URL extraction first before downloading.

## Overview

This guide covers essential PDF processing operations using Python libraries and command-line tools.

## URL-First Extraction

If the user provides a URL pointing to a PDF, extract it without downloading:

```
web_extract(url="https://example.com/report.pdf")
```

Fall back to download + local processing only if `web_extract` returns empty or errors.

---

## Fast Extraction: pymupdf (fitz)

**Best for**: Text-native PDFs (digital, not scanned). Install: `pip install pymupdf` (~25 MB).

```python
import fitz  # pymupdf

doc = fitz.open("document.pdf")
print(f"Pages: {doc.page_count}")

# Extract all text (fast)
full_text = "\n".join(page.get_text() for page in doc)

# Extract with layout blocks (tables, columns)
for page in doc:
    blocks = page.get_text("blocks")  # (x0,y0,x1,y1,text,block_no,block_type)
    for block in blocks:
        print(block[4])  # text content

# Extract images
for page in doc:
    for img in page.get_images():
        xref = img[0]
        base = doc.extract_image(xref)
        with open(f"img_{xref}.{base['ext']}", "wb") as f:
            f.write(base["image"])
```

pymupdf is 5-10× faster than pypdf for text extraction and preserves layout better.

---

## OCR Extraction: marker-pdf

**Best for**: Scanned PDFs, image-only PDFs, or documents where `pymupdf` returns garbled text.
Install: `pip install marker-pdf` (~5 GB with models).

```bash
# Single file
marker_single document.pdf output_dir/ --batch_multiplier 2

# Batch
marker input_dir/ output_dir/ --workers 4
```

Outputs Markdown with preserved headings, tables, and code blocks.

**Decision signal**: Run `pymupdf` first. If extracted text has <50% printable characters or looks like garbage, switch to `marker-pdf`.

---

## NLP Editing: nano-pdf

**Best for**: Semantic search, sentence-level edits, keyword replacement in text-native PDFs.
Install: `pip install nano-pdf`.

```python
from nano_pdf import NanoPDF

doc = NanoPDF("document.pdf")

# Search sentences
results = doc.search("termination clause", top_k=5)
for r in results:
    print(r.page, r.text, r.score)

# Replace text (produces new PDF)
doc.replace("old phrase", "new phrase", output="modified.pdf")
```

---

## 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}")
```

#### 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:
                df = pd.DataFrame(table[1:], columns=table[0])
                all_tables.append(df)

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
c.drawString(100, height - 100, "Hello World!")
c.line(100, height - 140, 400, height - 140)
c.save()
```

#### Subscripts and Superscripts

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

Use ReportLab's XML markup tags instead:
```python
from reportlab.platypus import Paragraph
from reportlab.lib.styles import getSampleStyleSheet
styles = getSampleStyleSheet()
chemical = Paragraph("H<sub>2</sub>O", styles['Normal'])
squared = Paragraph("x<super>2</super> + y<super>2</super>", styles['Normal'])
```

## PDF Form Processing

### Check if PDF has fillable fields
```bash
python scripts/check_fillable_fields.py document.pdf
```

### Extract form field info
```bash
python scripts/extract_form_field_info.py document.pdf
```

### Extract form structure (non-fillable PDFs)
```bash
python scripts/extract_form_structure.py document.pdf
```

### Fill form fields
```bash
python scripts/fill_fillable_fields.py document.pdf output.pdf --fields '{"field_name": "value"}'
```

### Fill with annotations (non-fillable PDFs)
```bash
python scripts/fill_pdf_form_with_annotations.py document.pdf output.pdf --data '{"x,y": "text"}'
```

### Validate bounding boxes
```bash
python scripts/check_bounding_boxes.py document.pdf
```

### Convert PDF to images
```bash
python scripts/convert_pdf_to_images.py document.pdf output_dir/ --dpi 150
```

### Create validation image with overlays
```bash
python scripts/create_validation_image.py document.pdf output.png
```

## Command-Line Tools

### pdftotext (poppler-utils)
```bash
pdftotext input.pdf output.txt              # Extract text
pdftotext -layout input.pdf output.txt      # Preserve layout
pdftotext -f 1 -l 5 input.pdf output.txt   # Pages 1-5
```

### qpdf
```bash
qpdf --empty --pages file1.pdf file2.pdf -- merged.pdf     # Merge
qpdf input.pdf --pages . 1-5 -- pages1-5.pdf               # Split
qpdf input.pdf output.pdf --rotate=+90:1                    # Rotate
qpdf --password=mypassword --decrypt encrypted.pdf out.pdf  # Decrypt
```

## Common Tasks

### Extract Text from Scanned PDFs (OCR)
```python
import pytesseract
from pdf2image import convert_from_path

images = convert_from_path('scanned.pdf')
text = ""
for i, image in enumerate(images):
    text += f"Page {i+1}:\n"
    text += pytesseract.image_to_string(image)
    text += "\n\n"
```

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

watermark = PdfReader("watermark.pdf").pages[0]
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)
```

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

reader = PdfReader("input.pdf")
writer = PdfWriter()
for page in reader.pages:
    writer.add_page(page)
writer.encrypt("userpassword", "ownerpassword")
with open("encrypted.pdf", "wb") as output:
    writer.write(output)
```

## Quick Reference

| Task | Best Tool | Command/Code |
|------|-----------|--------------|
| URL → text | web_extract | `web_extract(url=...)` |
| Fast text extraction | pymupdf | `fitz.open(...).get_text()` |
| Scanned / OCR | marker-pdf | `marker_single doc.pdf out/` |
| Semantic search/edit | nano-pdf | `NanoPDF(...).search(...)` |
| Merge PDFs | pypdf | `writer.add_page(page)` |
| Split PDFs | pypdf | One page per file |
| Extract text (layout) | pdfplumber | `page.extract_text()` |
| Extract tables | pdfplumber | `page.extract_tables()` |
| Create PDFs | reportlab | Canvas or Platypus |
| Fill forms | scripts | `fill_fillable_fields.py` |