A high-performance tool for automating the extraction, grouping, and cost estimation of quantities from BIM and CAD datasets.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install qto-report
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install qto-report using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
This skill provides a robust framework for generating comprehensive Quantity Take-Off (QTO) reports directly from BIM/CAD data. By leveraging Openclaw Skills, developers and construction professionals can automate the tedious process of manual quantity extraction, enabling high-precision 5D BIM workflows that integrate geometry, scheduling, and cost estimation.
The skill follows the Data-Driven Construction (DDC) methodology to organize BIM elements by category, level, or material. It supports multiple data sources including Revit CSV exports and IFC files via IfcOpenShell, making it an essential component for automated cost estimation and project planning in the AEC industry.
Ensure you have Python 3 installed on your system. This skill utilizes the Openclaw Skills ecosystem and requires specific libraries for data manipulation and IFC processing.
pip install pandas openpyxl ifcopenshell
The skill processes BIM element data into a structured format for reporting. The following table describes the standard output schema generated by the Openclaw Skills workflow:
| Field | Type | Description |
|---|---|---|
| Category | String | BIM category classification (e.g., Wall, Floor, Column) |
| Volume | Float | Total calculated volume in cubic units |
| Area | Float | Total surface area in square units |
| Count | Integer | Total number of unique elements identified in the group |
| Total_Cost | Float | Calculated financial estimate (Quantity × Unit Price) |
| Material | String | Specific material assignment for detailed breakdown analysis |
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A Python-powered skill for fetching, analyzing, and estimating construction material prices via open APIs.

A professional ETL skill for converting unstructured construction PDFs into machine-readable Excel, CSV, and JSON formats.

A powerful Pandas-based toolkit designed to process, analyze, and report on large-scale construction and BIM datasets.

A sophisticated integration engine for consolidating global and local construction datasets into actionable project workflows.

A specialized framework for building Retrieval-Augmented Generation systems tailored to construction industry documentation and knowledge bases.

A powerful utility for converting Revit RVT and RFA files into structured Excel databases for seamless BIM data analysis.








































