ocr-and-documents: Productivity for Claude Code | AI Skill Market | AI Skill Market

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Active Users

Developers

Time Saved

2+ hrs

Per week

Quality

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Excellent

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PDF & Document Extraction

For DOCX: use

python-docx

(parses actual document structure, far better than OCR). For PPTX: see the

powerpoint

skill (uses

python-pptx

with full slide/notes support). This skill covers PDFs and scanned documents.

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"])
web_extract(urls=["https://example.com/report.pdf"])

This handles PDF-to-markdown conversion 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	❌	✅
Headers/footers removal	❌	✅
Reading order detection	❌	✅
Images extraction	✅ (embedded)	✅ (with context)
Images → text (OCR)	❌	✅
EPUB	✅	✅
Markdown output	✅ (via pymupdf4llm)	✅ (native, higher quality)
Install size	~25MB	~3-5GB (PyTorch + models)
Speed	Instant	~1-14s/page (CPU), ~0.2s/page (GPU)

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

If the user needs marker capabilities but the system lacks ~5GB free disk:

"This document needs OCR/advanced extraction (marker-pdf), which requires ~5GB for PyTorch and models. Your system has [X]GB free. Options: free up space, provide a URL so I can use web_extract, or I can try pymupdf which works for text-based PDFs but not scanned documents or equations."

pymupdf (lightweight)

pip install pymupdf pymupdf4llm

Via helper script:

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)

# Check disk space first
python scripts/extract_marker.py --check

pip install marker-pdf

Via helper script:

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 (installed with marker-pdf):

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

Arxiv Papers

# Abstract only (fast)
web_extract(urls=["https://arxiv.org/abs/2402.03300"])

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

# Search
web_search(query="arxiv GRPO reinforcement learning 2026")

Split, Merge & Search

pymupdf handles these natively — use

execute_code

or inline Python:

# 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)")
        print(page.get_text("text"))

No extra dependencies needed — pymupdf covers split, merge, search, and text extraction in one package.

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, complex layouts — install only when needed
Both helper scripts accept
```
--help
```
for full usage
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)

License

MIT

Quick Start

Install Command

mkdir -p ~/.hermes/skills/productivity/ocr-and-documents && curl -o ~/.hermes/skills/productivity/ocr-and-documents/SKILL.md https://raw.githubusercontent.com/NousResearch/hermes-agent/main/skills/productivity/ocr-and-documents/SKILL.md

ocr-and-documents

AI Skill Market Insights

Be Part of the 0+ Developer Community

PDF & Document Extraction

Step 1: Remote URL Available?

Step 2: Choose Local Extractor

pymupdf (lightweight)

marker-pdf (high-quality OCR)

Arxiv Papers

Split, Merge & Search

Notes

License

Quick Start

Install Command

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Superpowers

Claude Command Suite

Linear Issue Management

Linear CLI Integration

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