Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
Qto Report
Generate Quantity Take-Off (QTO) reports from BIM/CAD data. Extract volumes, areas, counts by category. Group elements, apply calculation rules, and create c...
skill openclawclawhub Free
Generate Quantity Take-Off (QTO) reports from BIM/CAD data. Extract volumes, areas, counts by category. Group elements, apply calculation rules, and create c...
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Install for OpenClaw
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- Download the package from Yavira.
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Package facts
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- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- claw.json, instructions.md, SKILL.md
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 2.1.0
Provenance
- Publisher
- datadrivenconstruction
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Documentation
Overview
Based on DDC methodology (Chapter 3.2), this skill automates the extraction and grouping of quantities from BIM/CAD data. QTO is the foundation for cost estimation, scheduling, and project planning in construction. Book Reference: "Quantity Take-Off и автоматическое создание смет" / "QTO and Automated Estimates" "QTO Quantity Take-Off: группировка данных по атрибутам позволяет автоматически извлекать объемы и количества из BIM-моделей для расчета стоимости." — DDC Book, Chapter 3.2
5D BIM Concept
The QTO process is central to 5D BIM: 3D: Geometry (volume, area, length) 4D: Time (schedule integration) 5D: Cost (quantity × unit price)
Quick Start
import pandas as pd # Load BIM element data df = pd.read_csv("revit_export.csv") # Generate QTO by category qto = df.groupby('Category').agg({ 'Volume': 'sum', 'Area': 'sum', 'ElementId': 'count' }).rename(columns={'ElementId': 'Count'}) # Calculate cost (if unit prices available) qto['Unit_Price'] = [150, 80, 450, 200] # $/m³ qto['Total_Cost'] = qto['Volume'] * qto['Unit_Price'] qto.to_excel("qto_report.xlsx")
Basic QTO by Category
import pandas as pd def generate_qto(df, group_by='Category'): """ Generate Quantity Take-Off grouped by specified column Args: df: DataFrame with BIM elements group_by: Column(s) to group by Returns: QTO summary DataFrame """ # Define aggregations based on available columns agg_dict = {} if 'Volume' in df.columns: agg_dict['Volume'] = 'sum' if 'Area' in df.columns: agg_dict['Area'] = 'sum' if 'Length' in df.columns: agg_dict['Length'] = 'sum' if 'Count' in df.columns: agg_dict['Count'] = 'sum' else: agg_dict['ElementId'] = 'count' qto = df.groupby(group_by).agg(agg_dict) if 'ElementId' in agg_dict: qto = qto.rename(columns={'ElementId': 'Count'}) return qto.round(2) # Usage qto = generate_qto(df, group_by='Category') print(qto)
Multi-Level QTO
def generate_multi_level_qto(df): """Generate QTO grouped by multiple levels""" qto = df.groupby(['Level', 'Category', 'Material']).agg({ 'Volume': ['sum', 'count'], 'Area': 'sum' }).round(2) # Flatten column names qto.columns = ['Volume_m3', 'Element_Count', 'Area_m2'] # Add percentages qto['Volume_Pct'] = (qto['Volume_m3'] / qto['Volume_m3'].sum() * 100).round(1) return qto.sort_values('Volume_m3', ascending=False) # Usage qto = generate_multi_level_qto(df) qto.to_excel("qto_multi_level.xlsx")
QTO with Pivot Table
def generate_qto_pivot(df, values='Volume', index='Level', columns='Category'): """Generate QTO as pivot table""" pivot = pd.pivot_table( df, values=values, index=index, columns=columns, aggfunc='sum', fill_value=0, margins=True, margins_name='TOTAL' ).round(2) return pivot # Usage - Volume by Level and Category qto_pivot = generate_qto_pivot(df, values='Volume') qto_pivot.to_excel("qto_pivot.xlsx")
Apply Unit Prices
def calculate_cost_from_qto(qto_df, prices_df, quantity_col='Volume'): """ Calculate costs by applying unit prices to quantities Args: qto_df: QTO DataFrame with quantities prices_df: DataFrame with Category and Unit_Price quantity_col: Column containing quantities """ # Merge with prices result = qto_df.reset_index().merge( prices_df, on='Category', how='left' ) # Calculate costs result['Total_Cost'] = result[quantity_col] * result['Unit_Price'] result['Cost_Pct'] = (result['Total_Cost'] / result['Total_Cost'].sum() * 100).round(1) # Summary grand_total = result['Total_Cost'].sum() print(f"Grand Total: ${grand_total:,.2f}") return result # Unit prices database prices = pd.DataFrame({ 'Category': ['Wall', 'Floor', 'Column', 'Beam', 'Foundation'], 'Unit_Price': [150, 80, 450, 200, 120], # $/m³ 'Unit': ['m³', 'm³', 'm³', 'm³', 'm³'] }) # Calculate cost_estimate = calculate_cost_from_qto(qto, prices) cost_estimate.to_excel("cost_estimate.xlsx", index=False)
Apply Rules from Excel
def apply_excel_rules(df, rules_path): """ Apply calculation rules defined in Excel file Excel format: | Category | Formula_Type | Factor | Unit | | Wall | volume | 1.05 | m³ | | Floor | area | 1.10 | m² | """ rules = pd.read_excel(rules_path) results = [] for _, rule in rules.iterrows(): category = rule['Category'] formula_type = rule['Formula_Type'] factor = rule['Factor'] category_data = df[df['Category'] == category].copy() if formula_type == 'volume': category_data['Quantity'] = category_data['Volume'] * factor elif formula_type == 'area': category_data['Quantity'] = category_data['Area'] * factor elif formula_type == 'length': category_data['Quantity'] = category_data['Length'] * factor elif formula_type == 'count': category_data['Quantity'] = category_data.groupby('Category').ngroup() + 1 category_data['Unit'] = rule['Unit'] results.append(category_data) return pd.concat(results, ignore_index=True) # Usage df_with_quantities = apply_excel_rules(df, "calculation_rules.xlsx")
From Revit Export (CSV)
def process_revit_export(csv_path): """Process standard Revit schedule export""" df = pd.read_csv(csv_path) # Standardize column names column_mapping = { 'Family and Type': 'Type', 'Volume': 'Volume', 'Area': 'Area', 'Count': 'Count', 'Level': 'Level', 'Category': 'Category' } df = df.rename(columns={ k: v for k, v in column_mapping.items() if k in df.columns }) # Convert volume from cubic feet to cubic meters (if needed) if 'Volume' in df.columns: # Revit exports in cubic feet by default df['Volume_m3'] = df['Volume'] * 0.0283168 return df # Usage df = process_revit_export("revit_schedule.csv") qto = generate_qto(df)
From IFC Export
# Using IfcOpenShell import ifcopenshell import pandas as pd def extract_qto_from_ifc(ifc_path): """Extract quantities from IFC file""" ifc = ifcopenshell.open(ifc_path) elements = [] for element in ifc.by_type("IfcBuildingElement"): # Get properties props = { 'GlobalId': element.GlobalId, 'Name': element.Name, 'Type': element.is_a(), 'Material': None, 'Volume': None, 'Area': None } # Extract quantities from property sets for definition in element.IsDefinedBy: if definition.is_a('IfcRelDefinesByProperties'): pset = definition.RelatingPropertyDefinition if pset.is_a('IfcElementQuantity'): for qty in pset.Quantities: if qty.is_a('IfcQuantityVolume'): props['Volume'] = qty.VolumeValue elif qty.is_a('IfcQuantityArea'): props['Area'] = qty.AreaValue elements.append(props) return pd.DataFrame(elements) # Usage df = extract_qto_from_ifc("model.ifc") qto = generate_qto(df, group_by='Type')
Detailed Material Breakdown
def material_breakdown_qto(df): """Detailed breakdown by material type""" breakdown = df.groupby(['Category', 'Material', 'Type']).agg({ 'Volume': 'sum', 'Area': 'sum', 'ElementId': 'nunique' }).rename(columns={'ElementId': 'Unique_Elements'}) # Add subtotals for each category category_totals = df.groupby('Category').agg({ 'Volume': 'sum', 'Area': 'sum' }) breakdown['Category_Volume_Pct'] = breakdown.apply( lambda row: (row['Volume'] / category_totals.loc[row.name[0], 'Volume'] * 100), axis=1 ).round(1) return breakdown # Usage material_qto = material_breakdown_qto(df) material_qto.to_excel("material_breakdown.xlsx")
QTO with Waste Factors
def qto_with_waste(df, waste_factors): """ Apply waste factors to quantities Args: waste_factors: dict like {'Concrete': 1.05, 'Steel': 1.03} """ qto = df.groupby(['Category', 'Material']).agg({ 'Volume': 'sum' }).reset_index() # Apply waste factors qto['Waste_Factor'] = qto['Material'].map(waste_factors).fillna(1.0) qto['Net_Volume'] = qto['Volume'] qto['Gross_Volume'] = qto['Volume'] * qto['Waste_Factor'] qto['Waste_Volume'] = qto['Gross_Volume'] - qto['Net_Volume'] return qto # Usage waste = {'Concrete': 1.05, 'Brick': 1.08, 'Steel': 1.03} qto = qto_with_waste(df, waste)
QTO Comparison (Design vs As-Built)
def compare_qto(design_df, asbuilt_df, group_by='Category'): """Compare designed vs as-built quantities""" design_qto = design_df.groupby(group_by)['Volume'].sum() asbuilt_qto = asbuilt_df.groupby(group_by)['Volume'].sum() comparison = pd.DataFrame({ 'Design': design_qto, 'AsBuilt': asbuilt_qto }) comparison['Difference'] = comparison['AsBuilt'] - comparison['Design'] comparison['Variance_%'] = ( (comparison['AsBuilt'] - comparison['Design']) / comparison['Design'] * 100 ).round(1) return comparison # Usage comparison = compare_qto(design_df, asbuilt_df) print(comparison)
Export to Multiple Formats
def export_qto_report(qto_df, base_name, include_charts=True): """Export QTO to Excel with formatting and charts""" from openpyxl import Workbook from openpyxl.chart import BarChart, Reference # Excel with multiple sheets with pd.ExcelWriter(f"{base_name}.xlsx", engine='openpyxl') as writer: # Summary sheet qto_df.to_excel(writer, sheet_name='Summary') # Detailed data if hasattr(qto_df, 'reset_index'): qto_df.reset_index().to_excel( writer, sheet_name='Details', index=False ) # CSV for integration qto_df.to_csv(f"{base_name}.csv") # JSON for API qto_df.reset_index().to_json( f"{base_name}.json", orient='records', indent=2 ) print(f"Exported: {base_name}.xlsx, .csv, .json") # Usage export_qto_report(qto, "project_qto")
Quick Reference
TaskCodeBasic QTOdf.groupby('Category')['Volume'].sum()Multi-column QTOdf.groupby(['Level', 'Category']).agg({...})Pivot QTOpd.pivot_table(df, values='Volume', ...)Apply pricesqto.merge(prices, on='Category')Calculate costdf['Cost'] = df['Volume'] * df['Unit_Price']Add waste factordf['Gross'] = df['Net'] * waste_factor
Resources
Book: "Data-Driven Construction" by Artem Boiko, Chapter 3.2 Website: https://datadrivenconstruction.io IfcOpenShell: https://ifcopenshell.org
Next Steps
See cost-estimation-resource for detailed cost calculations See auto-estimate-generator for automated estimate creation See gantt-chart for 4D scheduling integration See co2-estimation for carbon footprint calculations
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs1 Config
- SKILL.md
- instructions.md
- claw.json