Extract quantities from BIM/CAD data for cost estimation. Group by type, level, zone. Generate QTO reports.

⬇ 0 downloads ★ 0 stars Unverified but indexed

Install for OpenClaw

Quick setup

Download the package from Yavira.
Extract the archive and review SKILL.md first.
Import or place the package into your OpenClaw setup.

Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: claw.json, instructions.md, SKILL.md

Validation

Use the Yavira download entry.
Review SKILL.md after the package is downloaded.
Confirm the extracted package contains the expected setup assets.

Install with your agent

Agent handoff

Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.

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.

New install

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

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.

Open Send to Agent page Open JSON manifest Open Markdown brief

Trust & source

Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 2.1.0

Provenance

Publisher: datadrivenconstruction
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 7 sections Open source page

Overview

Quantity Takeoff (QTO) extracts measurable quantities from BIM models. This skill processes BIM exports to generate grouped quantity reports for cost estimation.

Python Implementation

import pandas as pd import numpy as np from typing import Dict, Any, List, Optional, Tuple from dataclasses import dataclass, field from enum import Enum class QTOUnit(Enum): """Quantity takeoff measurement units.""" COUNT = "ea" LENGTH = "m" AREA = "m2" VOLUME = "m3" WEIGHT = "kg" LINEAR_FOOT = "lf" SQUARE_FOOT = "sf" CUBIC_YARD = "cy" @dataclass class QTOItem: """Single QTO line item.""" category: str type_name: str description: str quantity: float unit: str level: Optional[str] = None material: Optional[str] = None element_count: int = 0 @dataclass class QTOReport: """Complete QTO report.""" project_name: str items: List[QTOItem] total_elements: int categories: int generated_date: str class BIMQuantityTakeoff: """Extract quantities from BIM data.""" # Column mappings for different BIM exports COLUMN_MAPPINGS = { 'type': ['Type Name', 'TypeName', 'type_name', 'Family and Type', 'IfcType'], 'category': ['Category', 'category', 'IfcClass', 'Element Category'], 'level': ['Level', 'level', 'Building Storey', 'BuildingStorey', 'Floor'], 'volume': ['Volume', 'volume', 'Volume (m³)', 'Qty_Volume'], 'area': ['Area', 'area', 'Surface Area', 'Area (m²)', 'Qty_Area'], 'length': ['Length', 'length', 'Length (m)', 'Qty_Length'], 'count': ['Count', 'count', 'Quantity', 'ElementCount'], 'material': ['Material', 'material', 'Structural Material', 'MaterialName'] } def __init__(self, df: pd.DataFrame): """Initialize with BIM data DataFrame.""" self.df = df self.column_map = self._detect_columns() def _detect_columns(self) -> Dict[str, str]: """Detect which columns exist in data.""" mapping = {} for standard, variants in self.COLUMN_MAPPINGS.items(): for variant in variants: if variant in self.df.columns: mapping[standard] = variant break return mapping def get_column(self, standard_name: str) -> Optional[str]: """Get actual column name from standard name.""" return self.column_map.get(standard_name) def group_by_type(self, sum_column: str = 'volume') -> pd.DataFrame: """Group quantities by type name.""" type_col = self.get_column('type') qty_col = self.get_column(sum_column) if type_col is None: raise ValueError("Type column not found") if qty_col is None: # Fall back to count result = self.df.groupby(type_col).size().reset_index(name='count') else: result = self.df.groupby(type_col).agg({ qty_col: 'sum' }).reset_index() result['count'] = self.df.groupby(type_col).size().values result.columns = ['Type', 'Quantity', 'Count'] if len(result.columns) == 3 else ['Type', 'Count'] return result.sort_values('Count', ascending=False) def group_by_category(self, sum_column: str = 'volume') -> pd.DataFrame: """Group quantities by category.""" cat_col = self.get_column('category') qty_col = self.get_column(sum_column) if cat_col is None: raise ValueError("Category column not found") agg_dict = {} if qty_col: agg_dict[qty_col] = 'sum' if agg_dict: result = self.df.groupby(cat_col).agg(agg_dict).reset_index() result['count'] = self.df.groupby(cat_col).size().values else: result = self.df.groupby(cat_col).size().reset_index(name='count') return result.sort_values('count', ascending=False) def group_by_level(self, sum_column: str = 'volume') -> pd.DataFrame: """Group quantities by building level.""" level_col = self.get_column('level') qty_col = self.get_column(sum_column) if level_col is None: raise ValueError("Level column not found") agg_dict = {} if qty_col: agg_dict[qty_col] = 'sum' if agg_dict: result = self.df.groupby(level_col).agg(agg_dict).reset_index() result['count'] = self.df.groupby(level_col).size().values else: result = self.df.groupby(level_col).size().reset_index(name='count') return result def pivot_by_level_and_type(self) -> pd.DataFrame: """Create pivot table: levels as rows, types as columns.""" level_col = self.get_column('level') type_col = self.get_column('type') if level_col is None or type_col is None: raise ValueError("Level or Type column not found") pivot = pd.crosstab( self.df[level_col], self.df[type_col], margins=True ) return pivot def filter_by_category(self, categories: List[str]) -> 'BIMQuantityTakeoff': """Filter to specific categories.""" cat_col = self.get_column('category') if cat_col is None: raise ValueError("Category column not found") filtered_df = self.df[self.df[cat_col].isin(categories)] return BIMQuantityTakeoff(filtered_df) def filter_by_level(self, levels: List[str]) -> 'BIMQuantityTakeoff': """Filter to specific levels.""" level_col = self.get_column('level') if level_col is None: raise ValueError("Level column not found") filtered_df = self.df[self.df[level_col].isin(levels)] return BIMQuantityTakeoff(filtered_df) def get_walls(self) -> pd.DataFrame: """Get wall quantities.""" cat_col = self.get_column('category') if cat_col: walls = self.df[self.df[cat_col].str.contains('Wall', case=False, na=False)] return BIMQuantityTakeoff(walls).group_by_type() return pd.DataFrame() def get_floors(self) -> pd.DataFrame: """Get floor/slab quantities.""" cat_col = self.get_column('category') if cat_col: floors = self.df[self.df[cat_col].str.contains('Floor|Slab', case=False, na=False)] return BIMQuantityTakeoff(floors).group_by_type() return pd.DataFrame() def get_doors(self) -> pd.DataFrame: """Get door quantities.""" cat_col = self.get_column('category') if cat_col: doors = self.df[self.df[cat_col].str.contains('Door', case=False, na=False)] return BIMQuantityTakeoff(doors).group_by_type() return pd.DataFrame() def get_windows(self) -> pd.DataFrame: """Get window quantities.""" cat_col = self.get_column('category') if cat_col: windows = self.df[self.df[cat_col].str.contains('Window', case=False, na=False)] return BIMQuantityTakeoff(windows).group_by_type() return pd.DataFrame() def generate_report(self, project_name: str = "Project") -> QTOReport: """Generate complete QTO report.""" from datetime import datetime items = [] type_col = self.get_column('type') cat_col = self.get_column('category') level_col = self.get_column('level') vol_col = self.get_column('volume') area_col = self.get_column('area') mat_col = self.get_column('material') # Group by type grouped = self.df.groupby(type_col if type_col else self.df.columns[0]) for type_name, group in grouped: # Determine primary quantity qty = 0 unit = QTOUnit.COUNT.value if vol_col and vol_col in group.columns: qty = group[vol_col].sum() unit = QTOUnit.VOLUME.value elif area_col and area_col in group.columns: qty = group[area_col].sum() unit = QTOUnit.AREA.value else: qty = len(group) unit = QTOUnit.COUNT.value # Get category and material category = group[cat_col].iloc[0] if cat_col and cat_col in group.columns else "" material = group[mat_col].iloc[0] if mat_col and mat_col in group.columns else "" level = group[level_col].iloc[0] if level_col and level_col in group.columns else "" items.append(QTOItem( category=str(category), type_name=str(type_name), description=str(type_name), quantity=round(qty, 2), unit=unit, level=str(level) if level else None, material=str(material) if material else None, element_count=len(group) )) return QTOReport( project_name=project_name, items=items, total_elements=len(self.df), categories=self.df[cat_col].nunique() if cat_col else 0, generated_date=datetime.now().isoformat() ) def to_excel(self, output_path: str, project_name: str = "Project"): """Export QTO to Excel with multiple sheets.""" with pd.ExcelWriter(output_path, engine='openpyxl') as writer: # Summary by category self.group_by_category().to_excel( writer, sheet_name='By Category', index=False) # Summary by type self.group_by_type().to_excel( writer, sheet_name='By Type', index=False) # Level breakdown try: self.pivot_by_level_and_type().to_excel( writer, sheet_name='Level-Type Matrix') except: pass # Walls walls = self.get_walls() if not walls.empty: walls.to_excel(writer, sheet_name='Walls', index=False) # Doors and Windows doors = self.get_doors() if not doors.empty: doors.to_excel(writer, sheet_name='Doors', index=False) windows = self.get_windows() if not windows.empty: windows.to_excel(writer, sheet_name='Windows', index=False) return output_path

Quick Start

# Load BIM export df = pd.read_excel("revit_export.xlsx") # Initialize QTO qto = BIMQuantityTakeoff(df) # Get quantities by type by_type = qto.group_by_type() print(by_type.head(10)) # Get wall schedule walls = qto.get_walls() print(walls)

1. Full QTO Report

qto = BIMQuantityTakeoff(df) report = qto.generate_report("Office Building") print(f"Elements: {report.total_elements}") for item in report.items[:5]: print(f"{item.type_name}: {item.quantity} {item.unit}")

2. Level-by-Level Analysis

pivot = qto.pivot_by_level_and_type() print(pivot)

3. Export to Excel

qto.to_excel("qto_report.xlsx", "My Project")

Resources

DDC Book: Chapter 3.2 - Quantity Take-Off

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 Primary doc
instructions.md Docs
claw.json Config