pdf-tools

name	pdf-tools
description	PDF workflows: extract text from PDFs/scans (pymupdf, marker-pdf, OCR), edit PDF text/typos/titles via NL prompts (nano-pdf), split/merge/search PDFs.
version	1.0.0
author	Hermes Agent
license	MIT
platforms	["linux","macos","windows"]
metadata	{"hermes":{"tags":["PDF","Documents","OCR","Text-Extraction","Editing","Research","Arxiv","Productivity"],"related_skills":["powerpoint"]}}

PDF Tools — Extract, Edit, Split, Merge

Two workflows for working with PDFs:

1. Extract text — from text-based or scanned PDFs (pymupdf, marker-pdf)
2. Edit text — fix typos, change titles, update content (nano-pdf)

Plus: split, merge, search PDFs with pymupdf.

---

Part 1: Extract Text from PDFs

Step 1: Remote URL Available?

If the document has a URL, always try web_extract first:

web_extract(urls=["https://arxiv.org/pdf/2402.03300"])

This handles PDF-to-markdown via Firecrawl with no local dependencies.

Only use local extraction when: the file is local, web_extract fails, or you need batch processing.

Step 2: Choose Local Extractor

Feature	pymupdf (~25MB)	marker-pdf (~3-5GB)
Text-based PDF	✅	✅
Scanned PDF (OCR)	❌	✅ (90+ languages)
Tables	✅ (basic)	✅ (high accuracy)
Equations / LaTeX	❌	✅
Code blocks	❌	✅
Forms	❌	✅
Install size	~25MB	~3-5GB (PyTorch + models)
Speed	Instant	~1-14s/page (CPU)

Decision: Use pymupdf unless you need OCR, equations, forms, or complex layout analysis.

pymupdf (lightweight)

pip install pymupdf pymupdf4llm

Via helper script (scripts/extract_pymdf.py):

python scripts/extract_pymupdf.py document.pdf              # Plain text
python scripts/extract_pymupdf.py document.pdf --markdown    # Markdown
python scripts/extract_pymupdf.py document.pdf --tables      # Tables
python scripts/extract_pymupdf.py document.pdf --images out/ # Extract images
python scripts/extract_pymupdf.py document.pdf --metadata    # Title, author, pages
python scripts/extract_pymupdf.py document.pdf --pages 0-4   # Specific pages

Inline:

python3 -c "
import pymupdf
doc = pymupdf.open('document.pdf')
for page in doc:
    print(page.get_text())
"

marker-pdf (high-quality OCR)

pip install marker-pdf

Via helper script (scripts/extract_marker.py):

python scripts/extract_marker.py document.pdf                # Markdown
python scripts/extract_marker.py document.pdf --json         # JSON with metadata
python scripts/extract_marker.py document.pdf --output_dir out/  # Save images
python scripts/extract_marker.py scanned.pdf                 # Scanned PDF (OCR)
python scripts/extract_marker.py document.pdf --use_llm      # LLM-boosted accuracy

CLI:

marker_single document.pdf --output_dir ./output
marker /path/to/folder --workers 4    # Batch

Arxiv Papers

web_extract(urls=["https://arxiv.org/abs/2402.03300"])   # Abstract
web_extract(urls=["https://arxiv.org/pdf/2402.03300"])   # Full paper
web_search(query="arxiv GRPO reinforcement learning 2026")  # Search

---

Part 2: Edit PDF Text

Edit PDFs using natural-language instructions via nano-pdf.

Prerequisites

uv pip install nano-pdf  # or pip install nano-pdf

Usage

nano-pdf edit <file.pdf> <page_number> "<instruction>"

Examples

# Change a title on page 1
nano-pdf edit deck.pdf 1 "Change the title to 'Q3 Results' and fix the typo in the subtitle"

# Update a date on a specific page
nano-pdf edit report.pdf 3 "Update the date from January to February 2026"

# Fix content
nano-pdf edit contract.pdf 2 "Change the client name from 'Acme Corp' to 'Acme Industries'"

Notes

• Page numbers may be 0-based or 1-based — if the edit hits the wrong page, retry with ±1
• Always verify the output PDF after editing
• The tool uses an LLM under the hood — requires an API key
• Works well for text changes; complex layout modifications may need a different approach

---

Part 3: Split, Merge & Search

pymupdf handles these natively:

# Split: extract pages 1-5 to a new PDF
import pymupdf
doc = pymupdf.open("report.pdf")
new = pymupdf.open()
for i in range(5):
    new.insert_pdf(doc, from_page=i, to_page=i)
new.save("pages_1-5.pdf")

# Merge multiple PDFs
import pymupdf
result = pymupdf.open()
for path in ["a.pdf", "b.pdf", "c.pdf"]:
    result.insert_pdf(pymupdf.open(path))
result.save("merged.pdf")

# Search for text across all pages
import pymupdf
doc = pymupdf.open("report.pdf")
for i, page in enumerate(doc):
    results = page.search_for("revenue")
    if results:
        print(f"Page {i+1}: {len(results)} match(es)")

---

Notes

• web_extract is always first choice for URLs
• pymupdf is the safe default — instant, no models, works everywhere
• marker-pdf is for OCR, scanned docs, equations — install only when needed
• marker-pdf downloads ~2.5GB of models to ~/.cache/huggingface/ on first use
• For Word docs: pip install python-docx (better than OCR — parses actual structure)
• For PowerPoint: see the powerpoint skill (uses python-pptx)