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Extract text from PDFs/scans (pymupdf, marker-pdf).
Passed 15 of 19 test cases with the skill, and 5 of 19 without it.
and 9 more in the full report
12 fixed5 passed either way4 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 12 test cases the skill fixed. Same model, same prompt, with and without the skill.
I have a distorted historical scan 'old_letter.pdf' and need maximum extraction accuracy using LLM boosting via the marker script. What command flag is passed?
| Checked | Without skill | With skill |
|---|---|---|
| Uses the --use_llm flag with scripts/extract_marker.py | Missed | Passed |
The instructions your model reads when the skill loads.
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.
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.
| 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.
test cases passed, vs 5 / 19 without
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