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Process PDF files - extract text, create PDFs, merge documents. Use when user asks to read PDF, create PDF, or work with PDF files.

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local-test-mcp
1464 skills
Install
$ npx -y skills add wulawulu/learn-claude-code-rs --skill pdf --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/pdf

Context preview

The summary Claude sees to decide when to auto-load this skill.

Process PDF files - extract text, create PDFs, merge documents. Use when user asks to read PDF, create PDF, or work with PDF files.

SKILL.md

pdf.SKILL.md
name: pdf
description: Process PDF files - extract text, create PDFs, merge documents. Use when user asks to read PDF, create PDF, or work with PDF files.

PDF Processing Skill

You now have expertise in PDF manipulation. Follow these workflows:

Reading PDFs

**Option 1: Quick text extraction (preferred)**

# 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**

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)**

# 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**

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**

# Using wkhtmltopdf
wkhtmltopdf input.html output.pdf

# Or with Python
python3 -c "
import pdfkit
pdfkit.from_file('input.html', 'output.pdf')
"

Merging PDFs

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

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 empty

Read more
Ships withlocal-test-mcp

A progressive AI Agent Harness tutorial written in Rust. This repository is a Rust-oriented learning path for building an agent harness.

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Repo: wulawulu/learn-claude-code-rs

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