Llm Document Extraction

What This Skill Does

The LLM Document Extraction skill serves as an intelligent bridge between static, unstructured construction documents and actionable digital intelligence. By leveraging advanced Large Language Models (LLMs), it parses complex PDFs such as RFIs, submittals, specifications, and contracts to isolate critical project data. Instead of manual data entry, the agent automatically identifies and maps entities—like RFI dates, approval statuses, manufacturer details, and contractual clauses—into clean, machine-readable JSON or Excel formats. This minimizes human error, accelerates administrative workflows, and ensures that project managers can access project history and requirements instantly. It transforms raw, fragmented documents into a unified, queryable database for better construction oversight.

Installation

To integrate this skill into your OpenClaw environment, execute the following command in your terminal: clawhub install openclaw/skills/skills/datadrivenconstruction/llm-document-extraction

Use Cases

This skill is designed for high-volume document environments typical of modern construction projects:

• RFI Tracking: Extract questions, responses, and impacts to keep project logs updated.
• Submittal Management: Extract product data, model numbers, and approval codes from submittal packages.
• Contract Administration: Identify key deliverables, payment terms, and scope definitions.
• Specification Analysis: Quickly summarize material requirements and technical standards.
• Daily Reports: Parse field notes for weather, labor logs, and equipment utilization.

Example Prompts

1. "Analyze submittal-402.pdf and extract the manufacturer and model number; return the result as a JSON object."
2. "Review the RFI document uploaded and tell me if there is a schedule impact and what the proposed solution is."
3. "Extract all material requirements from the provided specification PDF and format them into a table for my project report."

Tips & Limitations

• Context Limits: The skill uses a truncate strategy for extremely large documents. For very long specs, consider processing by section or chapter.
• Schema Quality: The accuracy of the extraction depends heavily on the provided JSON schema. Be specific and define constraints (e.g., 'YYYY-MM-DD' formats) to improve output quality.
• Data Privacy: Ensure sensitive project documentation is handled according to your organization's security policies before processing.
• Model Selection: While GPT-4o is recommended for complex documents, smaller documents may be processed by more cost-effective models if accuracy thresholds allow.