Process PDF files - extract text, create PDFs, merge documents. Use when user asks to read PDF, create PDF, or work with PDF files.
Best use case
pdf is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Process PDF files - extract text, create PDFs, merge documents. Use when user asks to read PDF, create PDF, or work with PDF files.
Teams using pdf should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/pdf/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How pdf Compares
| Feature / Agent | Standard Approach | |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Process PDF files - extract text, create PDFs, merge documents. Use when user asks to read PDF, create PDF, or work with PDF files.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# PDF Processing Skill
You now have expertise in PDF manipulation. Follow these workflows:
## Reading PDFs
**Option 1: Quick text extraction (preferred)**
```bash
# Using pdftotext (poppler-utils)
pdftotext input.pdf - # Output to stdout
pdftotext input.pdf output.txt # Output to file
# If pdftotext not available, try:
python3 -c "
import fitz # PyMuPDF
doc = fitz.open('input.pdf')
for page in doc:
print(page.get_text())
"
```
**Option 2: Page-by-page with metadata**
```python
import fitz # pip install pymupdf
doc = fitz.open("input.pdf")
print(f"Pages: {len(doc)}")
print(f"Metadata: {doc.metadata}")
for i, page in enumerate(doc):
text = page.get_text()
print(f"--- Page {i+1} ---")
print(text)
```
## Creating PDFs
**Option 1: From Markdown (recommended)**
```bash
# Using pandoc
pandoc input.md -o output.pdf
# With custom styling
pandoc input.md -o output.pdf --pdf-engine=xelatex -V geometry:margin=1in
```
**Option 2: Programmatically**
```python
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
c = canvas.Canvas("output.pdf", pagesize=letter)
c.drawString(100, 750, "Hello, PDF!")
c.save()
```
**Option 3: From HTML**
```bash
# Using wkhtmltopdf
wkhtmltopdf input.html output.pdf
# Or with Python
python3 -c "
import pdfkit
pdfkit.from_file('input.html', 'output.pdf')
"
```
## Merging PDFs
```python
import fitz
result = fitz.open()
for pdf_path in ["file1.pdf", "file2.pdf", "file3.pdf"]:
doc = fitz.open(pdf_path)
result.insert_pdf(doc)
result.save("merged.pdf")
```
## Splitting PDFs
```python
import fitz
doc = fitz.open("input.pdf")
for i in range(len(doc)):
single = fitz.open()
single.insert_pdf(doc, from_page=i, to_page=i)
single.save(f"page_{i+1}.pdf")
```
## Key Libraries
| Task | Library | Install |
|------|---------|---------|
| Read/Write/Merge | PyMuPDF | `pip install pymupdf` |
| Create from scratch | ReportLab | `pip install reportlab` |
| HTML to PDF | pdfkit | `pip install pdfkit` + wkhtmltopdf |
| Text extraction | pdftotext | `brew install poppler` / `apt install poppler-utils` |
## Best Practices
1. **Always check if tools are installed** before using them
2. **Handle encoding issues** - PDFs may contain various character encodings
3. **Large PDFs**: Process page by page to avoid memory issues
4. **OCR for scanned PDFs**: Use `pytesseract` if text extraction returns emptyRelated Skills
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