Agent skill · data analytics · datadrivenconstruction
llm-document-extraction
Extract structured data from construction documents using LLMs. Process RFIs, submittals, contracts, specifications. Convert unstructured PDFs to structured JSON/Excel.
Why this skill is useful
Provides specific extraction schemas and Python scripts for processing construction documents that the AI wouldn't reliably generate on its own.
What it needs
Requires python3 installed locally. Requires openai account access. About 3k tokens when loaded. Last updated 2026-02-14. 265 stars on the source repository.
What this skill does
LLM Document Extraction Overview 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 Use Cases 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 Quick Start Extraction Schemas RFI Schema Submittal Schema Contract Schema Batch Processing with n8n Vision Model for Drawings RAG for Large Documents Requirements Resources OpenAI Vision: https://platform.openai.com/docs/guides/vision LangChain RAG: https://python.langchain.com/docs/tutorials/rag/
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills datadrivenconstruction/llm-document-extraction