OpenClaw

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 cost estimates automatically.

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查看来源 ↗安装与使用 SKILL.md ↗

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。

SkillHub CLI

skillhub install datadrivenconstruction/qto-report

打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

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.grou...

来源信息

Source: github
Repository: openclaw/skills
Path: skills/datadrivenconstruction/qto-report
Version: —
Commit: e25425d94d26a11c893445fb73ce9bfca353628a
分类来源: rules
状态: active
可见性: public

这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。

来源内容

SKILL.md

# 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

```python
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

```python
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.grou...

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