bim-qto

What This Skill Does

The bim-qto skill is an advanced analytical engine designed for Architecture, Engineering, and Construction (AEC) professionals. It bridges the gap between raw BIM (Building Information Modeling) data exports and actionable cost estimations. By ingesting structured data from sources like IFC files, Revit schedules, or CSV exports, the skill intelligently maps disparate column schemas to standardized measurement categories. It automatically calculates total volumes, areas, lengths, and component counts, allowing users to pivot their model data by level, zone, or specific material. This transforms massive, unstructured building datasets into clean, reportable quantity takeoff summaries, significantly reducing the manual effort typically required in manual spreadsheet estimation workflows.

Installation

To integrate this skill into your local environment, run the following command in your OpenClaw terminal: clawhub install openclaw/skills/skills/datadrivenconstruction/bim-qto

Use Cases

• Pre-construction Cost Estimation: Rapidly calculate the total concrete volume or wall surface area for bidding purposes without manual measurement.
• Material Procurement: Generate accurate lists of material requirements by grouping elements by type and category to assist in purchasing and supply chain management.
• Design Iteration Comparison: Compare quantity variations between different design phases or model versions to track budget impacts in real-time.
• Zone-based Logistics: Segment data by floor level or building zone to identify construction sequence requirements and localized resource needs.

Example Prompts

1. "Analyze the attached building model export and generate a QTO report grouped by Category and Level, summarizing total volumes for all concrete elements."
2. "Extract all window types from this CSV data, count the total units per type, and identify which level contains the highest quantity of floor-to-ceiling windows."
3. "Create a structured material takeoff report for the interior finishing phase based on the provided BIM data, filtering for only 'Area' and 'Length' based quantities."

Tips & Limitations

• Data Standardization: While the skill features an internal COLUMN_MAPPINGS dictionary to auto-detect common BIM formats, your raw data should use consistent headers for optimal parsing accuracy. If your export uses non-standard naming, you may need to update the mapping configuration.
• Preprocessing: Ensure that your BIM export has been properly cleaned (e.g., removing redundant modeled objects or non-physical helper geometry) before running the skill to avoid quantity inflation.
• Aggregation Scope: The current implementation excels at tabular calculations; for complex 3D spatial queries, ensure the geometry data is successfully flattened into your export schema.
• Unit Integrity: Always verify that input units (e.g., mm, cm, meters) are consistent throughout your source file, as the skill performs calculations based on the provided numeric values.