cad-to-data

What This Skill Does

The cad-to-data skill is designed to bridge the gap between complex Computer-Aided Design (CAD) and Building Information Modeling (BIM) files and structured, machine-readable data. Leveraging methodologies outlined in DDC, this skill can parse various industry-standard file formats including Revit (.rvt), IFC (.ifc), DWG (.dwg), DXF (.dxf), DGN (.dgn), NWD (.nwd), and STEP (.step).

Upon processing a CAD/BIM file, the skill extracts a wealth of information. This includes detailed data about individual elements such as their unique identifiers, names, categories (e.g., walls, doors, windows, pipes), and type names. It can also identify and report on associated metadata like levels, bounding box dimensions (width, depth, height, volume), and specific properties defined within the file. Furthermore, the skill quantifies elements by extracting material information (including names, categories, colors, areas, volumes, and custom properties) and calculating quantities. It also captures relationships between elements and details about CAD layers, including their visibility and element counts.

The output is a structured dataset, typically represented as a CADExtractionResult object in Python, which contains lists of extracted CADElement and CADLayer objects, alongside overall file information and statistics. This structured data is invaluable for downstream analysis, automation, and integration with other systems.

Installation

To install the cad-to-data skill, use the following command in your OpenClaw environment:

clawhub install openclaw/skills/skills/datadrivenconstruction/cad-to-data

This command will download and set up the skill, making it available for use within the OpenClaw AI agent.

Use Cases

This skill offers a wide range of applications across various industries, particularly in architecture, engineering, and construction (AEC):

• Data Analysis and Reporting: Quickly generate reports on element counts, material quantities, or spatial distributions from BIM models for project management or cost estimation.
• Digital Twin Creation: Extract foundational data from design models to populate and update digital twins, ensuring data consistency.
• Quantity Take-Off (QTO): Automate the process of extracting material quantities and element counts for accurate cost estimations and procurement.
• Clash Detection and Validation: Use extracted element data and relationships to perform automated checks and validations against design standards or other models.
• Asset Management: Extract key information about building components for asset tracking and maintenance planning throughout the building's lifecycle.
• Interoperability: Convert proprietary CAD/BIM formats into a standardized data structure that can be easily imported into other software or databases.
• Energy Performance Analysis: Extract building element geometry and material properties for use in energy simulation tools.

Example Prompts

1. "Analyze the main_building.rvt file and provide a summary of all wall elements, including their materials and bounding box dimensions."
2. "Extract all door and window elements from the ground_floor.ifc file, along with their associated properties and quantities."
3. "Convert the structural_beams.dwg file into a structured data format and list the number of elements per layer."

Tips & Limitations

• File Size: Processing very large or complex CAD/BIM files can be resource-intensive and may take a significant amount of time. Ensure your environment has adequate memory and processing power.
• File Quality: The accuracy and completeness of the extracted data are highly dependent on the quality and structure of the source CAD/BIM file. Poorly modeled or organized files may yield incomplete or inaccurate results.
• Format Support: While the skill supports a wide range of formats, specific versions or complex features within certain file types might have limitations. Always test with representative files.
• Custom Properties: The extraction of custom properties is dependent on how they are defined and stored within the source file. Not all custom parameters may be universally extractable.
• Level of Detail: The skill extracts geometric and property data. Highly detailed annotations or non-standard information might not be captured without specific configuration or extensions.
• Units: Be mindful of the units used in the source file and ensure consistency when interpreting quantities and dimensions in the extracted data. The skill generally aims to maintain native units.