drawing-analyzer

What This Skill Does

The Drawing Analyzer skill is an advanced agent capability designed to parse and interpret complex construction documentation, including PDF and DWG drawing sets. It automates the tedious manual labor of extracting structural and architectural data by identifying title blocks, dimensions, annotations, and symbols. The agent uses sophisticated pattern recognition to categorize elements like room tags, grid lines, and elevation markers, transforming static visual blueprints into structured machine-readable data objects that can be used for automated quantity takeoffs, quality assurance checks, and design standard compliance.

Installation

You can integrate this skill into your environment using the following command: clawhub install openclaw/skills/skills/datadrivenconstruction/drawing-analyzer

Use Cases

• Automated Takeoffs: Instantly count doors, windows, and equipment by identifying symbols across a full drawing set, saving hours of manual tallying.
• Design Review Automation: Programmatically verify that project titles, scales, and revision numbers align with corporate standards.
• Discrepancy Detection: Cross-reference extracted dimensions against a building information model (BIM) to identify inconsistencies between original designs and site-revised PDFs.
• Data Migration: Automatically populate project management software by pulling key metadata directly from the document title block.

Example Prompts

1. "Analyze the attached floor plan and provide a comprehensive schedule of all room tags and door annotations found in the document."
2. "Extract the total linear footage of walls from the provided PDF, paying special attention to dimensions marked with the '1/4 inch = 1 foot' scale."
3. "Compare the revision number in the title block of these three drawings and flag any sheets that are not on the latest 'Revision B' status."

Tips & Limitations

To ensure the highest accuracy, provide clean, high-resolution vector PDFs where possible. While the Drawing Analyzer is robust, hand-sketched drawings or low-resolution scans may result in lower confidence scores for automated symbol recognition. Always verify critical safety-related dimensions with a human reviewer. Currently, the skill supports standard imperial and metric annotation formats; if your project utilizes highly custom or non-standard symbols, you may need to provide additional configuration context to the agent to improve detection precision.