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Extract structured data from any document format using unified document processing.
Passed 16 of 21 test cases with the skill, and 14 of 21 without it.
3 fixed13 passed either way4 still failing1 broke1 errored
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
A newer attempt on gemini-3.6-flash on 10/15/2026 did not produce a publishable comparison (not comparable). The snapshot above is the previously measured run.
One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.
Before executing a Python data extraction pipeline on `multi_format_docs.zip` containing PDF, DOCX, and HTML files, what shell command should be run to prepare the Python environment with all standard dependencies?
| Checked | Without skill | With skill |
|---|---|---|
| Executes pip install specifying pdfplumber, python-docx, beautifulsoup4, lxml, and openpyxl. | Missed | Passed |
The instructions your model reads when the skill loads.
Extract structured data from documents in any format: PDF, DOCX, HTML, TXT, images, and more, using plain Python libraries (pdfplumber, PyMuPDF, python-docx, BeautifulSoup, Tesseract OCR). Converts unstructured or semi-structured content into clean JSON, CSV, or other structured formats. Handles invoices, forms, reports, and free-text documents.
When a user asks you to extract data from a document, follow this process:
# Determine file type
file document.pdf
# Install dependencies based on format
pip install pdfplumber python-docx beautifulsoup4 lxml openpyxl pymupdf
Library selection by format:
pdfplumber (text + tables), PyMuPDF (import pymupdf, older code uses import fitz) for complex layouts and speedpython-docxbeautifulsoup4 with lxmlopenpyxl or pandaspytesseract with Pillow; it only wraps the Tesseract program, which must be installed separately (apt install tesseract-ocr, brew install tesseract)PDF extraction:
import pdfplumber
with pdfplumber.open("document.pdf") as pdf:
for i, page in enumerate(pdf.pages):
text = page.extract_text()
print(f"--- Page {i+1} ---")
print(text)
test cases passed, vs 14 / 21 without
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