Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
Ifc To Excel
Convert IFC files (2x3, 4x1, 4x3) to Excel databases using IfcExporter CLI. Extract BIM data, properties, and geometry without proprietary software.
skill openclawclawhub Free
Convert IFC files (2x3, 4x1, 4x3) to Excel databases using IfcExporter CLI. Extract BIM data, properties, and geometry without proprietary software.
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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.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- claw.json, instructions.md, SKILL.md
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- Review SKILL.md after the package is downloaded.
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 2.0.0
Provenance
- Publisher
- datadrivenconstruction
- Source page
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Documentation
Problem Statement
IFC (Industry Foundation Classes) is the open BIM standard, but: Reading IFC requires specialized software Property extraction needs programming knowledge Batch processing is manual and time-consuming Integration with analytics tools is complex
Solution
IfcExporter.exe converts IFC files to structured Excel databases, making BIM data accessible for analysis, validation, and reporting.
Business Value
Open standard - Process any IFC file (2x3, 4x, 4.3) No licenses - Works offline without BIM software Data extraction - All properties, quantities, materials 3D geometry - Export to Collada DAE format Pipeline ready - Integrate with ETL workflows
CLI Syntax
IfcExporter.exe <input_ifc> [options]
Supported IFC Versions
VersionSchemaDescriptionIFC2x3MVDMost common exchange formatIFC4ADD1Enhanced propertiesIFC4x1AlignmentInfrastructure supportIFC4x3LatestFull infrastructure
Output Formats
OutputDescription.xlsxExcel database with elements and properties.daeCollada 3D geometry with matching IDs
Options
OptionDescriptionbboxInclude element bounding boxes-no-xlsxSkip Excel export-no-colladaSkip 3D geometry export
Examples
# Basic conversion (XLSX + DAE) IfcExporter.exe "C:\Models\Building.ifc" # With bounding boxes IfcExporter.exe "C:\Models\Building.ifc" bbox # Excel only (no 3D geometry) IfcExporter.exe "C:\Models\Building.ifc" -no-collada # Batch processing for /R "C:\IFC_Models" %f in (*.ifc) do IfcExporter.exe "%f" bbox
Python Integration
import subprocess import pandas as pd from pathlib import Path from typing import List, Optional, Dict, Any, Set from dataclasses import dataclass, field from enum import Enum import json class IFCVersion(Enum): """IFC schema versions.""" IFC2X3 = "IFC2X3" IFC4 = "IFC4" IFC4X1 = "IFC4X1" IFC4X3 = "IFC4X3" class IFCEntityType(Enum): """Common IFC entity types.""" IFCWALL = "IfcWall" IFCWALLSTANDARDCASE = "IfcWallStandardCase" IFCSLAB = "IfcSlab" IFCCOLUMN = "IfcColumn" IFCBEAM = "IfcBeam" IFCDOOR = "IfcDoor" IFCWINDOW = "IfcWindow" IFCROOF = "IfcRoof" IFCSTAIR = "IfcStair" IFCRAILING = "IfcRailing" IFCFURNISHINGELEMENT = "IfcFurnishingElement" IFCSPACE = "IfcSpace" IFCBUILDINGSTOREY = "IfcBuildingStorey" IFCBUILDING = "IfcBuilding" IFCSITE = "IfcSite" @dataclass class IFCElement: """Represents an IFC element.""" global_id: str ifc_type: str name: str description: Optional[str] object_type: Optional[str] level: Optional[str] # Quantities area: Optional[float] = None volume: Optional[float] = None length: Optional[float] = None height: Optional[float] = None width: Optional[float] = None # Bounding box (if exported) bbox_min_x: Optional[float] = None bbox_min_y: Optional[float] = None bbox_min_z: Optional[float] = None bbox_max_x: Optional[float] = None bbox_max_y: Optional[float] = None bbox_max_z: Optional[float] = None # Properties properties: Dict[str, Any] = field(default_factory=dict) materials: List[str] = field(default_factory=list) @dataclass class IFCProperty: """Represents an IFC property.""" pset_name: str property_name: str value: Any value_type: str @dataclass class IFCMaterial: """Represents an IFC material.""" name: str category: Optional[str] thickness: Optional[float] layer_position: Optional[int] class IFCExporter: """IFC to Excel converter using DDC IfcExporter CLI.""" def __init__(self, exporter_path: str = "IfcExporter.exe"): self.exporter = Path(exporter_path) if not self.exporter.exists(): raise FileNotFoundError(f"IfcExporter not found: {exporter_path}") def convert(self, ifc_file: str, include_bbox: bool = True, export_xlsx: bool = True, export_collada: bool = True) -> Path: """Convert IFC file to Excel.""" ifc_path = Path(ifc_file) if not ifc_path.exists(): raise FileNotFoundError(f"IFC file not found: {ifc_file}") cmd = [str(self.exporter), str(ifc_path)] if include_bbox: cmd.append("bbox") if not export_xlsx: cmd.append("-no-xlsx") if not export_collada: cmd.append("-no-collada") result = subprocess.run(cmd, capture_output=True, text=True) if result.returncode != 0: raise RuntimeError(f"Export failed: {result.stderr}") return ifc_path.with_suffix('.xlsx') def batch_convert(self, folder: str, include_subfolders: bool = True, include_bbox: bool = True) -> List[Dict[str, Any]]: """Convert all IFC files in folder.""" folder_path = Path(folder) pattern = "**/*.ifc" if include_subfolders else "*.ifc" results = [] for ifc_file in folder_path.glob(pattern): try: output = self.convert(str(ifc_file), include_bbox) results.append({ 'input': str(ifc_file), 'output': str(output), 'status': 'success' }) print(f"โ Converted: {ifc_file.name}") except Exception as e: results.append({ 'input': str(ifc_file), 'output': None, 'status': 'failed', 'error': str(e) }) print(f"โ Failed: {ifc_file.name} - {e}") return results def read_elements(self, xlsx_file: str) -> pd.DataFrame: """Read converted Excel as DataFrame.""" return pd.read_excel(xlsx_file, sheet_name="Elements") def get_element_types(self, xlsx_file: str) -> pd.DataFrame: """Get element type summary.""" df = self.read_elements(xlsx_file) if 'IfcType' not in df.columns: raise ValueError("IfcType column not found") summary = df.groupby('IfcType').agg({ 'GlobalId': 'count', 'Volume': 'sum' if 'Volume' in df.columns else 'count', 'Area': 'sum' if 'Area' in df.columns else 'count' }).reset_index() summary.columns = ['IFC_Type', 'Count', 'Total_Volume', 'Total_Area'] return summary.sort_values('Count', ascending=False) def get_levels(self, xlsx_file: str) -> pd.DataFrame: """Get building level summary.""" df = self.read_elements(xlsx_file) level_col = None for col in ['Level', 'BuildingStorey', 'IfcBuildingStorey']: if col in df.columns: level_col = col break if level_col is None: return pd.DataFrame(columns=['Level', 'Element_Count']) summary = df.groupby(level_col).agg({ 'GlobalId': 'count' }).reset_index() summary.columns = ['Level', 'Element_Count'] return summary def get_materials(self, xlsx_file: str) -> pd.DataFrame: """Get material summary.""" df = self.read_elements(xlsx_file) if 'Material' not in df.columns: return pd.DataFrame(columns=['Material', 'Count']) summary = df.groupby('Material').agg({ 'GlobalId': 'count' }).reset_index() summary.columns = ['Material', 'Element_Count'] return summary.sort_values('Element_Count', ascending=False) def get_quantities(self, xlsx_file: str, group_by: str = 'IfcType') -> pd.DataFrame: """Get quantity takeoff summary.""" df = self.read_elements(xlsx_file) if group_by not in df.columns: raise ValueError(f"Column {group_by} not found") agg_dict = {'GlobalId': 'count'} # Add numeric columns for aggregation numeric_cols = ['Volume', 'Area', 'Length', 'Width', 'Height'] for col in numeric_cols: if col in df.columns: agg_dict[col] = 'sum' summary = df.groupby(group_by).agg(agg_dict).reset_index() return summary def filter_by_type(self, xlsx_file: str, ifc_types: List[str]) -> pd.DataFrame: """Filter elements by IFC type.""" df = self.read_elements(xlsx_file) return df[df['IfcType'].isin(ifc_types)] def get_properties(self, xlsx_file: str, element_id: str) -> Dict[str, Any]: """Get all properties for specific element.""" df = self.read_elements(xlsx_file) element = df[df['GlobalId'] == element_id] if element.empty: return {} # Convert row to dictionary, excluding NaN values props = element.iloc[0].dropna().to_dict() return props def validate_ifc_data(self, xlsx_file: str) -> Dict[str, Any]: """Validate IFC data quality.""" df = self.read_elements(xlsx_file) validation = { 'total_elements': len(df), 'issues': [] } # Check for missing GlobalIds if 'GlobalId' in df.columns: missing_ids = df['GlobalId'].isna().sum() if missing_ids > 0: validation['issues'].append(f"{missing_ids} elements missing GlobalId") # Check for missing names if 'Name' in df.columns: missing_names = df['Name'].isna().sum() if missing_names > 0: validation['issues'].append(f"{missing_names} elements missing Name") # Check for zero quantities for col in ['Volume', 'Area']: if col in df.columns: zero_qty = (df[col] == 0).sum() if zero_qty > 0: validation['issues'].append(f"{zero_qty} elements with zero {col}") # Check for duplicate GlobalIds if 'GlobalId' in df.columns: duplicates = df['GlobalId'].duplicated().sum() if duplicates > 0: validation['issues'].append(f"{duplicates} duplicate GlobalIds") validation['is_valid'] = len(validation['issues']) == 0 return validation class IFCQuantityTakeoff: """Quantity takeoff from IFC data.""" def __init__(self, exporter: IFCExporter): self.exporter = exporter def generate_qto(self, ifc_file: str) -> Dict[str, pd.DataFrame]: """Generate complete quantity takeoff.""" xlsx = self.exporter.convert(ifc_file, include_bbox=True) df = self.exporter.read_elements(str(xlsx)) qto = {} # Walls walls = df[df['IfcType'].str.contains('Wall', case=False, na=False)] if not walls.empty: qto['Walls'] = self._summarize_elements(walls, 'Type Name') # Slabs slabs = df[df['IfcType'].str.contains('Slab', case=False, na=False)] if not slabs.empty: qto['Slabs'] = self._summarize_elements(slabs, 'Type Name') # Columns columns = df[df['IfcType'].str.contains('Column', case=False, na=False)] if not columns.empty: qto['Columns'] = self._summarize_elements(columns, 'Type Name') # Beams beams = df[df['IfcType'].str.contains('Beam', case=False, na=False)] if not beams.empty: qto['Beams'] = self._summarize_elements(beams, 'Type Name') # Doors doors = df[df['IfcType'].str.contains('Door', case=False, na=False)] if not doors.empty: qto['Doors'] = self._summarize_elements(doors, 'Type Name') # Windows windows = df[df['IfcType'].str.contains('Window', case=False, na=False)] if not windows.empty: qto['Windows'] = self._summarize_elements(windows, 'Type Name') return qto def _summarize_elements(self, df: pd.DataFrame, group_col: str) -> pd.DataFrame: """Summarize elements by grouping column.""" if group_col not in df.columns: group_col = 'IfcType' agg_dict = {'GlobalId': 'count'} for col in ['Volume', 'Area', 'Length']: if col in df.columns: agg_dict[col] = 'sum' summary = df.groupby(group_col).agg(agg_dict).reset_index() summary.rename(columns={'GlobalId': 'Count'}, inplace=True) return summary def export_to_excel(self, qto: Dict[str, pd.DataFrame], output_file: str): """Export QTO to multi-sheet Excel.""" with pd.ExcelWriter(output_file, engine='openpyxl') as writer: for sheet_name, df in qto.items(): df.to_excel(writer, sheet_name=sheet_name, index=False) # Convenience functions def convert_ifc_to_excel(ifc_file: str, exporter_path: str = "IfcExporter.exe") -> str: """Quick conversion of IFC to Excel.""" exporter = IFCExporter(exporter_path) output = exporter.convert(ifc_file) return str(output) def get_ifc_summary(xlsx_file: str) -> Dict[str, Any]: """Get summary of converted IFC data.""" df = pd.read_excel(xlsx_file, sheet_name="Elements") return { 'total_elements': len(df), 'ifc_types': df['IfcType'].nunique() if 'IfcType' in df.columns else 0, 'levels': df['Level'].nunique() if 'Level' in df.columns else 0, 'total_volume': df['Volume'].sum() if 'Volume' in df.columns else 0, 'total_area': df['Area'].sum() if 'Area' in df.columns else 0 }
Excel Sheets
SheetContentElementsAll IFC elements with propertiesTypesElement types summaryLevelsBuilding storey dataMaterialsMaterial assignmentsPropertySetsIFC property sets
Element Columns
ColumnTypeDescriptionGlobalIdstringIFC GUIDIfcTypestringIFC entity typeNamestringElement nameDescriptionstringElement descriptionLevelstringBuilding storeyMaterialstringPrimary materialVolumefloatVolume (mยณ)AreafloatSurface area (mยฒ)LengthfloatLength (m)HeightfloatHeight (m)WidthfloatWidth (m)
Quick Start
# Initialize exporter exporter = IFCExporter("C:/DDC/IfcExporter.exe") # Convert IFC to Excel xlsx = exporter.convert("C:/Models/Building.ifc", include_bbox=True) # Read elements df = exporter.read_elements(str(xlsx)) print(f"Total elements: {len(df)}") # Get element types types = exporter.get_element_types(str(xlsx)) print(types) # Get quantities by type qto = exporter.get_quantities(str(xlsx), group_by='IfcType') print(qto)
1. Model Validation
exporter = IFCExporter() xlsx = exporter.convert("model.ifc") validation = exporter.validate_ifc_data(str(xlsx)) if not validation['is_valid']: print("Issues found:") for issue in validation['issues']: print(f" - {issue}")
2. Quantity Takeoff
qto_generator = IFCQuantityTakeoff(exporter) qto = qto_generator.generate_qto("building.ifc") for category, data in qto.items(): print(f"\n{category}:") print(data.to_string(index=False))
3. Material Schedule
xlsx = exporter.convert("building.ifc") materials = exporter.get_materials(str(xlsx)) print(materials)
Integration with DDC Pipeline
# Full pipeline: IFC โ Excel โ Validation โ Cost Estimate exporter = IFCExporter("C:/DDC/IfcExporter.exe") # 1. Convert IFC xlsx = exporter.convert("project.ifc", include_bbox=True) # 2. Validate data validation = exporter.validate_ifc_data(str(xlsx)) print(f"Valid: {validation['is_valid']}") # 3. Generate QTO qto = IFCQuantityTakeoff(exporter) quantities = qto.generate_qto("project.ifc") # 4. Export for cost estimation qto.export_to_excel(quantities, "project_qto.xlsx")
Resources
GitHub: cad2data Pipeline IFC Standard: buildingSMART DDC Book: Chapter 2.4 - CAD/BIM Data Extraction
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
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