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Get Started Free →datadrivenconstruction/pdf-to-structured
Extract structured data from construction PDFs. Convert specifications, BOMs, schedules, and reports from PDF to Excel/CSV/JSON. Use OCR for scanned documents and pdfplumber for native PDFs.
Passed 14 of 22 test cases with the skill, and 12 of 22 without it.
3 fixed11 passed either way7 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.
I have a drawing PDF where the title block table is located in a specific coordinate region at the bottom right corner (x0=50, top=100, x1=550, bottom=400 in points). How should I crop and extract only that specific rectangular area using Python?
| Checked | Without skill | With skill |
|---|---|---|
| Uses within_bbox method to crop page area | Missed | Passed |
The instructions your model reads when the skill loads.
Based on DDC methodology (Chapter 2.4), this skill transforms unstructured PDF documents into structured formats suitable for analysis and integration. Construction projects generate vast amounts of PDF documentation - specifications, BOMs, schedules, and reports - that need to be extracted and processed.
Book Reference: "Преобразование данных в структурированную форму" / "Data Transformation to Structured Form"
"Преобразование данных из неструктурированной в структурированную форму — это и искусство, и наука. Этот процесс часто занимает значительную часть работы инженера по обработке данных." — DDC Book, Chapter 2.4
The conversion follows the ETL pattern:
import pdfplumber
import pandas as pd
# Extract table from PDF
with pdfplumber.open("construction_spec.pdf") as pdf:
page = pdf.pages[0]
table = page.extract_table()
df = pd.DataFrame(table[1:], columns=table[0])
df.to_excel("extracted_data.xlsx", index=False)
# Core libraries
pip install pdfplumber pandas openpyxl
# For scanned PDFs (OCR)
pip install pytesseract pdf2image
test cases passed, vs 12 / 22 without
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