Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_Construction --skill cad-to-datagit clone --depth 1 https://github.com/jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_ConstructionWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/cad-to-data)<a href="https://agentmods.dev/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/cad-to-data"><img src="https://agentmods.dev/badge/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/cad-to-data/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/cad-to-data"><img src="https://agentmods.dev/badge/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/cad-to-data.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00029 | $0.04415 |
| Opus 5 | $0.00015 | $0.02207 |
| Sonnet 5 | $0.00006 | $0.00883 |
| Haiku 4.5 | $0.00003 | $0.00441 |
Grade A, and why
cad-to-data scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to cad-to-data — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 655 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 655 lines · 29 tokens per session scan A 2867882b04e8
cad-to-data is a skill published in the GitHub repository jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_Construction (2 stars, last pushed 6mo ago), licensed MIT. It adds 29 tokens to every session and 4,415 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to cad-to-data, differing in 0 lines, and is treated as a copy.
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