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...

Security Scan

The skill's stated purpose (QTO from BIM/CAD data) matches its instructions and requirements; there are no unexplained credential requests, remote endpoints, or suspicious install steps.

Purpose & Capability

Name/description, required binary (python3) and optional IFC parser (ifcopenshell), and filesystem access are coherent with a QTO/reporting skill that reads BIM exports and produces spreadsheets.

Instruction Scope

SKILL.md contains only local data-processing instructions (pandas-based examples, reading CSV/Excel, applying rules, exporting results). It does not instruct the agent to read unrelated files, call external endpoints, or access secrets.

Install Mechanism

This is an instruction-only skill with no install spec or remote downloads. No archives or third‑party package installs are requested by the skill itself.

Credentials

The skill requests no environment variables or credentials. The manifest declares filesystem permission which is appropriate for reading user-provided BIM/CAD exports; nothing else is requested.

Persistence & Privilege

always is false and the skill is user-invocable. It does not request elevated or persistent platform privileges beyond normal filesystem access declared in claw.json.

Assessment

This skill appears internally consistent for generating QTO reports. Before installing: (1) confirm you trust the skill source/homepage if you plan to supply real project files; (2) ensure python3 and, if you need IFC parsing, ifcopenshell are installed from official sources; (3) do not provide sensitive credentials or unrelated system files—the skill operates on user-provided BIM/CAD exports (CSV/Excel/IFC); (4) test with a small non-sensitive sample project first to verify outputs and behavior.

Quantity Take-Off (QTO) Report Generation

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")

Core QTO Functions

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")

Cost Calculation from QTO

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")

BIM Data Extraction Patterns

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')

Advanced QTO Reports

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 Functions

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

Task	Code
Basic QTO	df.groupby('Category')['Volume'].sum()
Multi-column QTO	df.groupby(['Level', 'Category']).agg({...})
Pivot QTO	pd.pivot_table(df, values='Volume', ...)
Apply prices	qto.merge(prices, on='Category')
Calculate cost	df['Cost'] = df['Volume'] * df['Unit_Price']
Add waste factor	df['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