# Send Qto Report to your agent
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
## Fast path
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
## Suggested prompts
### New install

```text
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
```
### Upgrade existing

```text
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
```
## Machine-readable fields
```json
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  "item": {
    "slug": "qto-report",
    "name": "Qto Report",
    "source": "tencent",
    "type": "skill",
    "category": "数据分析",
    "sourceUrl": "https://clawhub.ai/datadrivenconstruction/qto-report",
    "canonicalUrl": "https://clawhub.ai/datadrivenconstruction/qto-report",
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      "claw.json",
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      "expiresAt": "2026-04-30T16:43:11.935Z",
      "httpStatus": 200,
      "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard",
      "contentType": "application/zip",
      "probeMethod": "head",
      "details": {
        "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard",
        "contentDisposition": "attachment; filename=\"4claw-imageboard-1.0.1.zip\"",
        "redirectLocation": null,
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      },
      "scope": "source",
      "summary": "Source download looks usable.",
      "detail": "Yavira can redirect you to the upstream package for this source.",
      "primaryActionLabel": "Download for OpenClaw",
      "primaryActionHref": "/downloads/qto-report"
    },
    "validation": {
      "installChecklist": [
        "Use the Yavira download entry.",
        "Review SKILL.md after the package is downloaded.",
        "Confirm the extracted package contains the expected setup assets."
      ],
      "postInstallChecks": [
        "Confirm the extracted package includes the expected docs or setup files.",
        "Validate the skill or prompts are available in your target agent workspace.",
        "Capture any manual follow-up steps the agent could not complete."
      ]
    }
  },
  "links": {
    "detailUrl": "https://openagent3.xyz/skills/qto-report",
    "downloadUrl": "https://openagent3.xyz/downloads/qto-report",
    "agentUrl": "https://openagent3.xyz/skills/qto-report/agent",
    "manifestUrl": "https://openagent3.xyz/skills/qto-report/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/qto-report/agent.md"
  }
}
```
## 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
## Trust
- Source: tencent
- Verification: Indexed source record
- Publisher: datadrivenconstruction
- Version: 2.1.0
## Source health
- Status: healthy
- Source download looks usable.
- Yavira can redirect you to the upstream package for this source.
- Health scope: source
- Reason: direct_download_ok
- Checked at: 2026-04-23T16:43:11.935Z
- Expires at: 2026-04-30T16:43:11.935Z
- Recommended action: Download for OpenClaw
## Links
- [Detail page](https://openagent3.xyz/skills/qto-report)
- [Send to Agent page](https://openagent3.xyz/skills/qto-report/agent)
- [JSON manifest](https://openagent3.xyz/skills/qto-report/agent.json)
- [Markdown brief](https://openagent3.xyz/skills/qto-report/agent.md)
- [Download page](https://openagent3.xyz/downloads/qto-report)