# Send Ifc Data Extraction 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
{
  "schemaVersion": "1.0",
  "item": {
    "slug": "ifc-data-extraction",
    "name": "Ifc Data Extraction",
    "source": "tencent",
    "type": "skill",
    "category": "AI 智能",
    "sourceUrl": "https://clawhub.ai/datadrivenconstruction/ifc-data-extraction",
    "canonicalUrl": "https://clawhub.ai/datadrivenconstruction/ifc-data-extraction",
    "targetPlatform": "OpenClaw"
  },
  "install": {
    "downloadUrl": "/downloads/ifc-data-extraction",
    "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=ifc-data-extraction",
    "sourcePlatform": "tencent",
    "targetPlatform": "OpenClaw",
    "packageFormat": "ZIP package",
    "primaryDoc": "SKILL.md",
    "includedAssets": [
      "claw.json",
      "instructions.md",
      "SKILL.md"
    ],
    "downloadMode": "redirect",
    "sourceHealth": {
      "source": "tencent",
      "slug": "ifc-data-extraction",
      "status": "healthy",
      "reason": "direct_download_ok",
      "recommendedAction": "download",
      "checkedAt": "2026-05-01T22:36:04.685Z",
      "expiresAt": "2026-05-08T22:36:04.685Z",
      "httpStatus": 200,
      "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=ifc-data-extraction",
      "contentType": "application/zip",
      "probeMethod": "head",
      "details": {
        "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=ifc-data-extraction",
        "contentDisposition": "attachment; filename=\"ifc-data-extraction-2.0.0.zip\"",
        "redirectLocation": null,
        "bodySnippet": null,
        "slug": "ifc-data-extraction"
      },
      "scope": "item",
      "summary": "Item download looks usable.",
      "detail": "Yavira can redirect you to the upstream package for this item.",
      "primaryActionLabel": "Download for OpenClaw",
      "primaryActionHref": "/downloads/ifc-data-extraction"
    },
    "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/ifc-data-extraction",
    "downloadUrl": "https://openagent3.xyz/downloads/ifc-data-extraction",
    "agentUrl": "https://openagent3.xyz/skills/ifc-data-extraction/agent",
    "manifestUrl": "https://openagent3.xyz/skills/ifc-data-extraction/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/ifc-data-extraction/agent.md"
  }
}
```
## Documentation

### Overview

This skill provides comprehensive IFC file parsing and data extraction using IfcOpenShell. Extract element data, quantities, properties, and relationships from BIM models for analysis and reporting.

Based on Open BIM Standards - Working with vendor-neutral IFC format for maximum interoperability.

"IFC является открытым стандартом для обмена BIM-данными, позволяющим извлекать информацию независимо от программного обеспечения."
— DDC Methodology

### Quick Start

import ifcopenshell
import ifcopenshell.util.element as element_util
import pandas as pd

# Open IFC file
ifc = ifcopenshell.open("model.ifc")

# Get project info
project = ifc.by_type("IfcProject")[0]
print(f"Project: {project.Name}")

# Extract all walls
walls = ifc.by_type("IfcWall")
print(f"Total walls: {len(walls)}")

# Get wall data
wall_data = []
for wall in walls:
    psets = element_util.get_psets(wall)
    wall_data.append({
        'GlobalId': wall.GlobalId,
        'Name': wall.Name,
        'Type': wall.is_a(),
        'Level': get_level(wall),
        'Properties': psets
    })

df = pd.DataFrame(wall_data)
print(df.head())

### Element Extractor Class

import ifcopenshell
import ifcopenshell.util.element as element_util
import ifcopenshell.util.placement as placement_util
import ifcopenshell.geom
import pandas as pd
from typing import List, Dict, Optional, Any

class IFCExtractor:
    """Extract data from IFC files"""

    def __init__(self, ifc_path: str):
        self.model = ifcopenshell.open(ifc_path)
        self.settings = ifcopenshell.geom.settings()

    def get_project_info(self) -> Dict:
        """Extract project metadata"""
        project = self.model.by_type("IfcProject")[0]
        site = self.model.by_type("IfcSite")
        building = self.model.by_type("IfcBuilding")

        return {
            'project_id': project.GlobalId,
            'project_name': project.Name,
            'description': project.Description,
            'site_count': len(site),
            'building_count': len(building),
            'schema': self.model.schema
        }

    def get_all_elements(self, element_types: List[str] = None) -> pd.DataFrame:
        """Extract all elements of specified types"""
        if element_types is None:
            element_types = [
                'IfcWall', 'IfcSlab', 'IfcColumn', 'IfcBeam',
                'IfcDoor', 'IfcWindow', 'IfcStair', 'IfcRoof'
            ]

        all_elements = []

        for ifc_type in element_types:
            elements = self.model.by_type(ifc_type)

            for elem in elements:
                data = self._extract_element_data(elem)
                data['IFC_Type'] = ifc_type
                all_elements.append(data)

        return pd.DataFrame(all_elements)

    def _extract_element_data(self, element) -> Dict:
        """Extract data from single element"""
        # Basic info
        data = {
            'GlobalId': element.GlobalId,
            'Name': element.Name,
            'Description': element.Description,
            'ObjectType': element.ObjectType if hasattr(element, 'ObjectType') else None
        }

        # Get level/storey
        data['Level'] = self._get_element_level(element)

        # Get material
        data['Material'] = self._get_element_material(element)

        # Get type
        data['TypeName'] = self._get_element_type(element)

        # Get all property sets
        psets = element_util.get_psets(element)
        data['PropertySets'] = psets

        # Extract common quantities
        base_quantities = psets.get('BaseQuantities', {})
        data.update({
            'Length': base_quantities.get('Length'),
            'Width': base_quantities.get('Width'),
            'Height': base_quantities.get('Height'),
            'Area': base_quantities.get('NetSideArea') or base_quantities.get('GrossArea'),
            'Volume': base_quantities.get('NetVolume') or base_quantities.get('GrossVolume')
        })

        return data

    def _get_element_level(self, element) -> Optional[str]:
        """Get the building storey for an element"""
        if hasattr(element, 'ContainedInStructure'):
            for rel in element.ContainedInStructure or []:
                if rel.RelatingStructure.is_a('IfcBuildingStorey'):
                    return rel.RelatingStructure.Name
        return None

    def _get_element_material(self, element) -> Optional[str]:
        """Get material name for element"""
        if hasattr(element, 'HasAssociations'):
            for rel in element.HasAssociations or []:
                if rel.is_a('IfcRelAssociatesMaterial'):
                    material = rel.RelatingMaterial
                    if hasattr(material, 'Name'):
                        return material.Name
                    elif hasattr(material, 'ForLayerSet'):
                        layers = material.ForLayerSet.MaterialLayers
                        if layers:
                            return layers[0].Material.Name
        return None

    def _get_element_type(self, element) -> Optional[str]:
        """Get element type name"""
        if hasattr(element, 'IsTypedBy'):
            for rel in element.IsTypedBy or []:
                return rel.RelatingType.Name
        return None

    def extract_quantities(self) -> pd.DataFrame:
        """Extract quantities for all elements"""
        elements = self.get_all_elements()

        # Group by category and level
        quantities = elements.groupby(['IFC_Type', 'Level']).agg({
            'GlobalId': 'count',
            'Volume': 'sum',
            'Area': 'sum',
            'Length': 'sum'
        }).rename(columns={'GlobalId': 'Count'}).reset_index()

        return quantities

    def extract_levels(self) -> pd.DataFrame:
        """Extract building levels/storeys"""
        storeys = self.model.by_type("IfcBuildingStorey")

        level_data = []
        for storey in storeys:
            level_data.append({
                'GlobalId': storey.GlobalId,
                'Name': storey.Name,
                'Elevation': storey.Elevation,
                'Description': storey.Description
            })

        return pd.DataFrame(level_data).sort_values('Elevation')

    def extract_spaces(self) -> pd.DataFrame:
        """Extract spaces/rooms"""
        spaces = self.model.by_type("IfcSpace")

        space_data = []
        for space in spaces:
            psets = element_util.get_psets(space)
            base_qty = psets.get('BaseQuantities', {})

            space_data.append({
                'GlobalId': space.GlobalId,
                'Name': space.Name,
                'LongName': space.LongName,
                'Level': self._get_element_level(space),
                'Area': base_qty.get('NetFloorArea'),
                'Volume': base_qty.get('NetVolume'),
                'Height': base_qty.get('Height')
            })

        return pd.DataFrame(space_data)

    def extract_materials(self) -> pd.DataFrame:
        """Extract material summary"""
        materials = {}

        for elem in self.model.by_type("IfcProduct"):
            material = self._get_element_material(elem)
            if material:
                if material not in materials:
                    materials[material] = {'count': 0, 'volume': 0}

                materials[material]['count'] += 1

                psets = element_util.get_psets(elem)
                volume = psets.get('BaseQuantities', {}).get('NetVolume', 0)
                if volume:
                    materials[material]['volume'] += volume

        return pd.DataFrame.from_dict(materials, orient='index').reset_index()

    def extract_relationships(self) -> pd.DataFrame:
        """Extract element relationships"""
        relationships = []

        # Spatial containment
        for rel in self.model.by_type("IfcRelContainedInSpatialStructure"):
            for elem in rel.RelatedElements:
                relationships.append({
                    'Element': elem.GlobalId,
                    'Element_Type': elem.is_a(),
                    'Relationship': 'ContainedIn',
                    'Related_To': rel.RelatingStructure.GlobalId,
                    'Related_Type': rel.RelatingStructure.is_a()
                })

        # Aggregation
        for rel in self.model.by_type("IfcRelAggregates"):
            for part in rel.RelatedObjects:
                relationships.append({
                    'Element': part.GlobalId,
                    'Element_Type': part.is_a(),
                    'Relationship': 'PartOf',
                    'Related_To': rel.RelatingObject.GlobalId,
                    'Related_Type': rel.RelatingObject.is_a()
                })

        return pd.DataFrame(relationships)

### Extract Geometry Data

import numpy as np

class IFCGeometryExtractor:
    """Extract geometry data from IFC elements"""

    def __init__(self, ifc_path: str):
        self.model = ifcopenshell.open(ifc_path)
        self.settings = ifcopenshell.geom.settings()
        self.settings.set(self.settings.USE_WORLD_COORDS, True)

    def get_element_geometry(self, element) -> Dict:
        """Extract geometry for single element"""
        try:
            shape = ifcopenshell.geom.create_shape(self.settings, element)

            verts = shape.geometry.verts
            faces = shape.geometry.faces

            # Calculate bounding box
            vertices = np.array(verts).reshape(-1, 3)
            min_coords = vertices.min(axis=0)
            max_coords = vertices.max(axis=0)
            dimensions = max_coords - min_coords

            return {
                'GlobalId': element.GlobalId,
                'vertices_count': len(vertices),
                'faces_count': len(faces) // 3,
                'min_x': min_coords[0],
                'min_y': min_coords[1],
                'min_z': min_coords[2],
                'max_x': max_coords[0],
                'max_y': max_coords[1],
                'max_z': max_coords[2],
                'length': dimensions[0],
                'width': dimensions[1],
                'height': dimensions[2],
                'center_x': (min_coords[0] + max_coords[0]) / 2,
                'center_y': (min_coords[1] + max_coords[1]) / 2,
                'center_z': (min_coords[2] + max_coords[2]) / 2
            }
        except:
            return {'GlobalId': element.GlobalId, 'error': 'Geometry extraction failed'}

    def get_bounding_boxes(self, element_type: str) -> pd.DataFrame:
        """Get bounding boxes for all elements of type"""
        elements = self.model.by_type(element_type)
        boxes = [self.get_element_geometry(e) for e in elements]
        return pd.DataFrame(boxes)

    def calculate_volumes(self, element_type: str) -> pd.DataFrame:
        """Calculate volumes using geometry"""
        elements = self.model.by_type(element_type)
        volumes = []

        for elem in elements:
            try:
                shape = ifcopenshell.geom.create_shape(self.settings, elem)
                # Calculate volume from mesh (simplified)
                verts = np.array(shape.geometry.verts).reshape(-1, 3)
                bbox_volume = np.prod(verts.max(axis=0) - verts.min(axis=0))

                volumes.append({
                    'GlobalId': elem.GlobalId,
                    'Name': elem.Name,
                    'BBox_Volume': bbox_volume
                })
            except:
                pass

        return pd.DataFrame(volumes)

### Export to Various Formats

class IFCExporter:
    """Export IFC data to various formats"""

    def __init__(self, extractor: IFCExtractor):
        self.extractor = extractor

    def to_excel(self, output_path: str, include_all: bool = True):
        """Export to Excel with multiple sheets"""
        with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
            # Project info
            project_info = pd.DataFrame([self.extractor.get_project_info()])
            project_info.to_excel(writer, sheet_name='Project', index=False)

            # All elements
            if include_all:
                elements = self.extractor.get_all_elements()
                elements.to_excel(writer, sheet_name='Elements', index=False)

            # Quantities
            quantities = self.extractor.extract_quantities()
            quantities.to_excel(writer, sheet_name='Quantities', index=False)

            # Levels
            levels = self.extractor.extract_levels()
            levels.to_excel(writer, sheet_name='Levels', index=False)

            # Spaces
            spaces = self.extractor.extract_spaces()
            spaces.to_excel(writer, sheet_name='Spaces', index=False)

            # Materials
            materials = self.extractor.extract_materials()
            materials.to_excel(writer, sheet_name='Materials', index=False)

        return output_path

    def to_csv(self, output_dir: str):
        """Export to multiple CSV files"""
        import os
        os.makedirs(output_dir, exist_ok=True)

        exports = {
            'elements.csv': self.extractor.get_all_elements(),
            'quantities.csv': self.extractor.extract_quantities(),
            'levels.csv': self.extractor.extract_levels(),
            'spaces.csv': self.extractor.extract_spaces(),
            'materials.csv': self.extractor.extract_materials()
        }

        for filename, df in exports.items():
            df.to_csv(os.path.join(output_dir, filename), index=False)

        return output_dir

    def to_json(self, output_path: str):
        """Export to JSON"""
        import json

        data = {
            'project': self.extractor.get_project_info(),
            'elements': self.extractor.get_all_elements().to_dict('records'),
            'quantities': self.extractor.extract_quantities().to_dict('records'),
            'levels': self.extractor.extract_levels().to_dict('records'),
            'materials': self.extractor.extract_materials().to_dict('records')
        }

        with open(output_path, 'w', encoding='utf-8') as f:
            json.dump(data, f, indent=2, default=str)

        return output_path

    def to_database(self, connection_string: str, table_prefix: str = 'ifc_'):
        """Export to SQL database"""
        from sqlalchemy import create_engine

        engine = create_engine(connection_string)

        tables = {
            f'{table_prefix}elements': self.extractor.get_all_elements(),
            f'{table_prefix}quantities': self.extractor.extract_quantities(),
            f'{table_prefix}levels': self.extractor.extract_levels(),
            f'{table_prefix}spaces': self.extractor.extract_spaces(),
            f'{table_prefix}materials': self.extractor.extract_materials()
        }

        for table_name, df in tables.items():
            # Remove complex columns for database storage
            simple_df = df.select_dtypes(exclude=['object']).copy()
            for col in df.columns:
                if df[col].dtype == 'object':
                    simple_df[col] = df[col].astype(str)

            simple_df.to_sql(table_name, engine, if_exists='replace', index=False)

        return list(tables.keys())

### Quick Reference

Element TypeCommon PropertiesQuantitiesIfcWallIsExternal, FireRatingLength, Height, Area, VolumeIfcSlabIsExternal, LoadBearingArea, Volume, PerimeterIfcColumnLoadBearingHeight, CrossSectionAreaIfcBeamLoadBearingLength, CrossSectionAreaIfcDoorFireRating, AcousticRatingWidth, HeightIfcWindowThermalTransmittanceWidth, Height, Area

### Property Set Lookup

# Common IFC Property Sets
PSETS = {
    'Pset_WallCommon': ['IsExternal', 'LoadBearing', 'FireRating'],
    'Pset_SlabCommon': ['IsExternal', 'LoadBearing', 'AcousticRating'],
    'Pset_ColumnCommon': ['IsExternal', 'LoadBearing'],
    'Pset_BeamCommon': ['LoadBearing', 'FireRating'],
    'Pset_DoorCommon': ['FireRating', 'AcousticRating', 'SecurityRating'],
    'Pset_WindowCommon': ['ThermalTransmittance', 'GlazingType'],
    'BaseQuantities': ['Length', 'Width', 'Height', 'Area', 'Volume']
}

### Resources

IfcOpenShell: https://ifcopenshell.org
IFC Standard: https://www.buildingsmart.org/standards/bsi-standards/industry-foundation-classes/
DDC Website: https://datadrivenconstruction.io

### Next Steps

See bim-validation-pipeline for validating extracted data
See qto-report for quantity take-off reports
See 4d-simulation for linking to schedules
## Trust
- Source: tencent
- Verification: Indexed source record
- Publisher: datadrivenconstruction
- Version: 2.0.0
## Source health
- Status: healthy
- Item download looks usable.
- Yavira can redirect you to the upstream package for this item.
- Health scope: item
- Reason: direct_download_ok
- Checked at: 2026-05-01T22:36:04.685Z
- Expires at: 2026-05-08T22:36:04.685Z
- Recommended action: Download for OpenClaw
## Links
- [Detail page](https://openagent3.xyz/skills/ifc-data-extraction)
- [Send to Agent page](https://openagent3.xyz/skills/ifc-data-extraction/agent)
- [JSON manifest](https://openagent3.xyz/skills/ifc-data-extraction/agent.json)
- [Markdown brief](https://openagent3.xyz/skills/ifc-data-extraction/agent.md)
- [Download page](https://openagent3.xyz/downloads/ifc-data-extraction)