cad-to-data
- Repo stars 155
- Author updated Live
- Author repo DDC_Skills_for_AI_Agents_in_Construction
- Domain
- Data
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @datadrivenconstruction · no license declared
- Token usage
- Moderate
- Setup complexity
- Guided setup
- External API key
- Not required
- Operating systems
- Windows
- Runtime requirements
- Python
- Permissions
-
- Read-only
- Write / modify
- Shell exec
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: cad-to-data
description: Convert CAD/BIM files to structured data. Extract element data from Revit, IFC, DWG, DGN files.…
category: data
runtime: Python
---
# cad-to-data output preview
## PART A: Task fit
- Use case: Convert CAD/BIM files to structured data. Extract element data from Revit, IFC, DWG, DGN files. Based on DDC methodology (Chapter 2.4), this skill converts CAD and BIM files to structured data, extracting element properties, quantities, and relationships from Revit, IFC, DWG, and DGN files. runs entirely locally; runs on Python. Works with Claude Code, Cu….
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Overview / Quick Start / Elements by Category” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Convert CAD/BIM files to structured data. Extract element data from Revit, IFC, DWG, DGN files. Based on DDC methodology (Chapter 2.4), this skill converts CAD and BIM files to structured data, extracting element properties, quantities, and relationships from Revit, IFC, DWG, and DGN files. runs entirely locally; runs on Python. Works with Claude Code, Cu…”.
- **02** When the source has headings, the agent prioritizes “Overview / Quick Start / Elements by Category” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files, run shell commands.
Start with a small task and check whether the result follows “Overview / Quick Start / Elements by Category”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: cad-to-data
description: Convert CAD/BIM files to structured data. Extract element data from Revit, IFC, DWG, DGN files.…
category: data
source: datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction
---
# cad-to-data
## When to use
- Convert CAD/BIM files to structured data. Extract element data from Revit, IFC, DWG, DGN files. Based on DDC methodolo…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Overview / Quick Start / Elements by Category” and keep inference separate from source facts.
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "cad-to-data" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Overview / Quick Start / Elements by Category
rules -> SKILL.md triggers / order / output contract
runtime -> Python | read files, write/modify files, run shell commands | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
} CAD To Data
Overview
Based on DDC methodology (Chapter 2.4), this skill converts CAD and BIM files to structured data, extracting element properties, quantities, and relationships from Revit, IFC, DWG, and DGN files.
Book Reference: "Преобразование данных в структурированную форму" / "Data Transformation to Structured Form"
Quick Start
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Dict, Optional, Any, Tuple, Generator
from datetime import datetime
import json
class CADFormat(Enum):
"""Supported CAD/BIM formats"""
IFC = "ifc"
RVT = "rvt"
DWG = "dwg"
DXF = "dxf"
DGN = "dgn"
NWD = "nwd"
STEP = "step"
class ElementCategory(Enum):
"""BIM element categories"""
WALL = "wall"
FLOOR = "floor"
ROOF = "roof"
CEILING = "ceiling"
DOOR = "door"
WINDOW = "window"
COLUMN = "column"
BEAM = "beam"
STAIR = "stair"
RAMP = "ramp"
FURNITURE = "furniture"
EQUIPMENT = "equipment"
PIPE = "pipe"
DUCT = "duct"
CABLE_TRAY = "cable_tray"
SPACE = "space"
GENERIC = "generic"
@dataclass
class Point3D:
"""3D point"""
x: float
y: float
z: float
@dataclass
class BoundingBox3D:
"""3D bounding box"""
min_point: Point3D
max_point: Point3D
@property
def width(self) -> float:
return abs(self.max_point.x - self.min_point.x)
@property
def depth(self) -> float:
return abs(self.max_point.y - self.min_point.y)
@property
def height(self) -> float:
return abs(self.max_point.z - self.min_point.z)
@property
def volume(self) -> float:
return self.width * self.depth * self.height
@dataclass
class MaterialInfo:
"""Material information"""
name: str
category: str
color: Optional[str] = None
area: float = 0.0
volume: float = 0.0
properties: Dict[str, Any] = field(default_factory=dict)
@dataclass
class CADElement:
"""Extracted CAD/BIM element"""
id: str
guid: str
name: str
category: ElementCategory
type_name: str
level: Optional[str] = None
bounding_box: Optional[BoundingBox3D] = None
properties: Dict[str, Any] = field(default_factory=dict)
quantities: Dict[str, float] = field(default_factory=dict)
materials: List[MaterialInfo] = field(default_factory=list)
relationships: Dict[str, List[str]] = field(default_factory=dict)
@dataclass
class CADLayer:
"""CAD layer information"""
name: str
color: Optional[str] = None
line_type: Optional[str] = None
visible: bool = True
element_count: int = 0
@dataclass
class CADExtractionResult:
"""Result of CAD extraction"""
file_path: str
file_format: CADFormat
elements: List[CADElement]
layers: List[CADLayer]
levels: List[str]
total_elements: int
categories: Dict[str, int]
extraction_time: float
metadata: Dict[str, Any] = field(default_factory=dict)
class IFCExtractor:
"""Extract data from IFC files"""
def __init__(self):
self.schema_version = "IFC4"
self.element_mapping = self._build_element_mapping()
def _build_element_mapping(self) -> Dict[str, ElementCategory]:
"""Map IFC types to categories"""
return {
"IfcWall": ElementCategory.WALL,
"IfcWallStandardCase": ElementCategory.WALL,
"IfcSlab": ElementCategory.FLOOR,
"IfcRoof": ElementCategory.ROOF,
"IfcCeiling": ElementCategory.CEILING,
"IfcDoor": ElementCategory.DOOR,
"IfcWindow": ElementCategory.WINDOW,
"IfcColumn": ElementCategory.COLUMN,
"IfcBeam": ElementCategory.BEAM,
"IfcStair": ElementCategory.STAIR,
"IfcRamp": ElementCategory.RAMP,
"IfcFurnishingElement": ElementCategory.FURNITURE,
"IfcPipeSegment": ElementCategory.PIPE,
"IfcDuctSegment": ElementCategory.DUCT,
"IfcCableCarrierSegment": ElementCategory.CABLE_TRAY,
"IfcSpace": ElementCategory.SPACE,
}
def extract(
self,
file_path: str,
categories: Optional[List[ElementCategory]] = None
) -> CADExtractionResult:
"""
Extract data from IFC file.
Args:
file_path: Path to IFC file
categories: Optional filter for categories
Returns:
Extraction result
"""
start_time = datetime.now()
# In production, use ifcopenshell:
# import ifcopenshell
# ifc_file = ifcopenshell.open(file_path)
# Simulated extraction
elements = self._simulate_ifc_elements()
# Filter by category if specified
if categories:
elements = [e for e in elements if e.category in categories]
# Build category counts
category_counts = {}
for element in elements:
cat = element.category.value
category_counts[cat] = category_counts.get(cat, 0) + 1
# Extract levels
levels = list(set(e.level for e in elements if e.level))
extraction_time = (datetime.now() - start_time).total_seconds()
return CADExtractionResult(
file_path=file_path,
file_format=CADFormat.IFC,
elements=elements,
layers=[], # IFC doesn't use layers in traditional sense
levels=levels,
total_elements=len(elements),
categories=category_counts,
extraction_time=extraction_time,
metadata={
"schema": self.schema_version,
"project_name": "Sample Project"
}
)
def _simulate_ifc_elements(self) -> List[CADElement]:
"""Simulate IFC element extraction"""
elements = []
# Sample walls
for i in range(10):
elements.append(CADElement(
id=f"wall_{i}",
guid=f"1234567890ABCDEF{i:04d}",
name=f"Basic Wall {i}",
category=ElementCategory.WALL,
type_name="Basic Wall:200mm Concrete",
level="Level 1",
bounding_box=BoundingBox3D(
min_point=Point3D(i * 5, 0, 0),
max_point=Point3D(i * 5 + 5, 0.2, 3)
),
properties={
"IsExternal": True,
"FireRating": "1 HR",
"LoadBearing": True
},
quantities={
"Length": 5.0,
"Height": 3.0,
"Width": 0.2,
"Area": 15.0,
"Volume": 3.0
},
materials=[
MaterialInfo(
name="Concrete",
category="Concrete",
area=15.0,
volume=3.0
)
]
))
# Sample doors
for i in range(5):
elements.append(CADElement(
id=f"door_{i}",
guid=f"DOOR0000000000{i:04d}",
name=f"Single Door {i}",
category=ElementCategory.DOOR,
type_name="Single Flush:900x2100",
level="Level 1",
properties={
"FireRating": "None",
"IsExternal": False
},
quantities={
"Width": 0.9,
"Height": 2.1,
"Area": 1.89
},
relationships={
"host_wall": [f"wall_{i}"]
}
))
# Sample spaces
for i in range(3):
elements.append(CADElement(
id=f"space_{i}",
guid=f"SPACE000000000{i:04d}",
name=f"Room {i+101}",
category=ElementCategory.SPACE,
type_name="Office",
level="Level 1",
quantities={
"Area": 25.0 + i * 5,
"Volume": 75.0 + i * 15,
"Perimeter": 20.0 + i * 2
},
properties={
"OccupancyType": "Office",
"DesignOccupancy": 4
}
))
return elements
def get_quantities(
self,
elements: List[CADElement],
quantity_type: str = "all"
) -> Dict[str, float]:
"""Aggregate quantities from elements"""
totals = {}
for element in elements:
for qty_name, qty_value in element.quantities.items():
if quantity_type == "all" or qty_name.lower() == quantity_type.lower():
key = f"{element.category.value}_{qty_name}"
totals[key] = totals.get(key, 0) + qty_value
return totals
class DWGExtractor:
"""Extract data from DWG/DXF files"""
def __init__(self):
self.supported_entities = ["LINE", "POLYLINE", "CIRCLE", "ARC", "TEXT", "MTEXT", "INSERT", "HATCH"]
def extract(
self,
file_path: str,
layers: Optional[List[str]] = None
) -> CADExtractionResult:
"""Extract data from DWG file"""
start_time = datetime.now()
# In production, use ezdxf:
# import ezdxf
# doc = ezdxf.readfile(file_path)
# Simulated extraction
elements, cad_layers = self._simulate_dwg_extraction()
# Filter by layers if specified
if layers:
elements = [e for e in elements if e.properties.get("layer") in layers]
extraction_time = (datetime.now() - start_time).total_seconds()
return CADExtractionResult(
file_path=file_path,
file_format=CADFormat.DWG,
elements=elements,
layers=cad_layers,
levels=[],
total_elements=len(elements),
categories={"generic": len(elements)},
extraction_time=extraction_time,
metadata={"units": "millimeters"}
)
def _simulate_dwg_extraction(self) -> Tuple[List[CADElement], List[CADLayer]]:
"""Simulate DWG extraction"""
elements = []
layers = [
CADLayer("Walls", "Red", "Continuous", True, 15),
CADLayer("Doors", "Blue", "Continuous", True, 8),
CADLayer("Windows", "Cyan", "Continuous", True, 12),
CADLayer("Dimensions", "Green", "Continuous", True, 50),
CADLayer("Text", "White", "Continuous", True, 25),
]
# Simulate polylines (walls)
for i in range(15):
elements.append(CADElement(
id=f"polyline_{i}",
guid=f"PL{i:08d}",
name=f"Polyline {i}",
category=ElementCategory.GENERIC,
type_name="POLYLINE",
properties={
"layer": "Walls",
"color": "Red",
"closed": True
},
quantities={
"Length": 10.5 + i * 0.5
}
))
return elements, layers
class CADDataConverter:
"""
Convert CAD/BIM files to structured data.
Based on DDC methodology Chapter 2.4.
"""
def __init__(self):
self.ifc_extractor = IFCExtractor()
self.dwg_extractor = DWGExtractor()
def convert(
self,
file_path: str,
output_format: str = "json"
) -> Dict[str, Any]:
"""
Convert CAD file to structured data.
Args:
file_path: Path to CAD file
output_format: Output format (json, csv, dataframe)
Returns:
Structured data
"""
# Detect file format
file_format = self._detect_format(file_path)
# Extract based on format
if file_format == CADFormat.IFC:
result = self.ifc_extractor.extract(file_path)
elif file_format in [CADFormat.DWG, CADFormat.DXF]:
result = self.dwg_extractor.extract(file_path)
else:
raise ValueError(f"Unsupported format: {file_format}")
# Convert to output format
return self._format_output(result, output_format)
def _detect_format(self, file_path: str) -> CADFormat:
"""Detect CAD file format"""
extension = file_path.lower().split(".")[-1]
format_map = {
"ifc": CADFormat.IFC,
"rvt": CADFormat.RVT,
"dwg": CADFormat.DWG,
"dxf": CADFormat.DXF,
"dgn": CADFormat.DGN,
"nwd": CADFormat.NWD,
}
return format_map.get(extension, CADFormat.IFC)
def _format_output(
self,
result: CADExtractionResult,
format: str
) -> Dict[str, Any]:
"""Format extraction result"""
output = {
"file": result.file_path,
"format": result.file_format.value,
"total_elements": result.total_elements,
"categories": result.categories,
"levels": result.levels,
"extraction_time": result.extraction_time,
"elements": []
}
for element in result.elements:
output["elements"].append({
"id": element.id,
"guid": element.guid,
"name": element.name,
"category": element.category.value,
"type": element.type_name,
"level": element.level,
"properties": element.properties,
"quantities": element.quantities,
"materials": [
{"name": m.name, "area": m.area, "volume": m.volume}
for m in element.materials
]
})
return output
def extract_quantities(
self,
file_path: str,
categories: Optional[List[ElementCategory]] = None
) -> Dict[str, Any]:
"""Extract quantity takeoff from CAD file"""
file_format = self._detect_format(file_path)
if file_format == CADFormat.IFC:
result = self.ifc_extractor.extract(file_path, categories)
else:
result = self.dwg_extractor.extract(file_path)
# Aggregate quantities by category
quantities = {}
for element in result.elements:
cat = element.category.value
if cat not in quantities:
quantities[cat] = {
"count": 0,
"totals": {}
}
quantities[cat]["count"] += 1
for qty_name, qty_value in element.quantities.items():
if qty_name not in quantities[cat]["totals"]:
quantities[cat]["totals"][qty_name] = 0
quantities[cat]["totals"][qty_name] += qty_value
return {
"file": file_path,
"quantities": quantities,
"summary": {
"total_elements": result.total_elements,
"categories": list(quantities.keys())
}
}
def extract_schedule(
self,
file_path: str,
category: ElementCategory,
fields: List[str]
) -> List[Dict]:
"""Extract schedule data for specific category"""
file_format = self._detect_format(file_path)
if file_format == CADFormat.IFC:
result = self.ifc_extractor.extract(file_path, [category])
else:
result = self.dwg_extractor.extract(file_path)
schedule = []
for element in result.elements:
if element.category == category:
row = {"id": element.id, "name": element.name, "type": element.type_name}
for field in fields:
if field in element.properties:
row[field] = element.properties[field]
elif field in element.quantities:
row[field] = element.quantities[field]
schedule.append(row)
return schedule
def export_to_json(
self,
result: CADExtractionResult,
output_path: str
):
"""Export extraction result to JSON file"""
output = self._format_output(result, "json")
with open(output_path, 'w') as f:
json.dump(output, f, indent=2)
def generate_report(self, result: CADExtractionResult) -> str:
"""Generate extraction report"""
report = f"""
# CAD Extraction Report
**File:** {result.file_path}
**Format:** {result.file_format.value}
**Total Elements:** {result.total_elements}
**Extraction Time:** {result.extraction_time:.2f}s
## Elements by Category
"""
for cat, count in result.categories.items():
report += f"- **{cat.title()}:** {count}\n"
if result.levels:
report += "\n## Levels\n"
for level in result.levels:
report += f"- {level}\n"
if result.layers:
report += "\n## Layers\n"
for layer in result.layers:
report += f"- {layer.name}: {layer.element_count} elements\n"
return report
Common Use Cases
Extract IFC Data
converter = CADDataConverter()
# Convert IFC to structured data
data = converter.convert("building.ifc", output_format="json")
print(f"Total elements: {data['total_elements']}")
print(f"Categories: {data['categories']}")
# Access elements
for element in data['elements'][:5]:
print(f" {element['name']}: {element['type']}")
Extract Quantities
quantities = converter.extract_quantities(
"building.ifc",
categories=[ElementCategory.WALL, ElementCategory.FLOOR]
)
print(f"Wall count: {quantities['quantities']['wall']['count']}")
print(f"Total wall area: {quantities['quantities']['wall']['totals']['Area']}")
Generate Schedule
door_schedule = converter.extract_schedule(
"building.ifc",
category=ElementCategory.DOOR,
fields=["Width", "Height", "FireRating", "IsExternal"]
)
for door in door_schedule:
print(f"{door['name']}: {door.get('Width')}x{door.get('Height')}")
Generate Report
ifc_extractor = IFCExtractor()
result = ifc_extractor.extract("building.ifc")
report = converter.generate_report(result)
print(report)
Quick Reference
| Component | Purpose |
|---|---|
CADDataConverter |
Main conversion engine |
IFCExtractor |
IFC file extraction |
DWGExtractor |
DWG/DXF extraction |
CADElement |
Extracted element data |
CADExtractionResult |
Complete extraction result |
ElementCategory |
BIM element categories |
Resources
- Book: "Data-Driven Construction" by Artem Boiko, Chapter 2.4
- Website: https://datadrivenconstruction.io
Next Steps
- Use image-to-data for image extraction
- Use qto-report for quantity reports
- Use bim-validation-pipeline for validation
Decide Fit First
Design Intent
How To Use It
Boundaries And Review