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Extract structured data from construction documents using LLMs. Process RFIs, submittals, contracts, specifications. Convert unstructured PDFs to structured JSON/Excel.
Passed 17 of 22 test cases with the skill, and 10 of 22 without it.
and 7 more in the full report
7 fixed10 passed either way5 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
When creating a Qdrant collection for storing construction contract chunks using LangChain vector stores, what collection name string should be assigned?
| Checked | Without skill | With skill |
|---|---|---|
| Sets vector store collection_name parameter to contract_docs | Missed | Passed |
The instructions your model reads when the skill loads.
Construction documents (RFIs, submittals, specs, contracts) contain critical data trapped in unstructured formats. This skill uses LLMs to extract structured data automatically.
"The construction industry is drowning in a flood of new data: the volume of information has grown from 15 zettabytes in 2015 to 181 zettabytes in 2025, and 90% of all existing data has been created in just the last few years." — Artem Boiko
| Document Type | Extract |
|---|---|
| RFI | Question, response, dates, parties |
| Submittal | Product specs, approval status, materials |
| Contract | Parties, amounts, dates, scope, clauses |
| Specification | Materials, standards, requirements |
| Daily Report | Weather, labor, equipment, progress |
from openai import OpenAI
import pdfplumber
import json
client = OpenAI()
def extract_from_pdf(pdf_path: str, extraction_schema: dict) -> dict:
"""Extract structured data from PDF using LLM"""
# Extract text from PDF
with pdfplumber.open(pdf_path) as pdf:
text = "\n".join(page.extract_text() for page in pdf.pages)
# Build extraction prompt
prompt = f"""
Extract the following information from this construction document.
Return ONLY valid JSON matching the schema.
Schema:
test cases passed, vs 10 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.