Dwg To Excel logo

Dwg To Excel

Community
datadrivenconstruction
dwg-to-excel

Convert AutoCAD DWG files (1983-2026) to Excel databases using DwgExporter CLI. Extract layers, blocks, attributes, and geometry data without Autodesk licenses.

Overview

Publisherdatadrivenconstruction
RepositoryDDC_Skills_for_AI_Agents_in_Construction
Skill namedwg-to-excel
Stars
365
Forks
91
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by datadrivenconstruction on GitHub. Read the source before you install it.

Installation

Install the Dwg To Excel AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/1_DDC_Toolkit/CAD-Converters/dwg-to-excel
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction.git /tmp/DDC_Skills_for_AI_Agents_in_Construction
mkdir -p .claude/skills
cp -r /tmp/DDC_Skills_for_AI_Agents_in_Construction/1_DDC_Toolkit/CAD-Converters/dwg-to-excel .claude/skills/dwg-to-excel
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dwg To Excel in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Dwg To Excel on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Dwg To Excel is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

DWG to Excel Conversion

Business Case

Problem Statement

AutoCAD DWG files contain valuable project data locked in proprietary format:

  • Layer structures with drawing organization
  • Block references with attribute data
  • Text annotations and dimensions
  • Geometric entities (lines, polylines, arcs)
  • External references (xrefs)

Extracting this data typically requires AutoCAD licenses or complex programming.

Solution

DwgExporter.exe converts DWG files to structured Excel databases offline, without Autodesk licenses.

Business Value

  • Zero license cost - No AutoCAD license required
  • Legacy support - Reads DWG files from 1983 to 2026
  • Data extraction - Layers, blocks, attributes, text, geometry
  • PDF export - Generate drawings from DWG layouts
  • Batch processing - Convert thousands of DWG files

Technical Implementation

CLI Syntax

bash
DwgExporter.exe <input_dwg> [options]

Output Formats

OutputDescription
.xlsxExcel database with all entities
.pdfPDF drawings from layouts

Supported Versions

Version RangeDescription
R12 (1992)Legacy DWG
R14 (1997)AutoCAD 14
2000-2002DWG 2000 format
2004-2006DWG 2004 format
2007-2009DWG 2007 format
2010-2012DWG 2010 format
2013-2017DWG 2013 format
2018-2026DWG 2018 format

Examples

bash
# Basic conversion
DwgExporter.exe "C:\Projects\FloorPlan.dwg"

# Export with PDF drawings
DwgExporter.exe "C:\Projects\FloorPlan.dwg" sheets2pdf

# Batch processing all DWG in folder
for /R "C:\Projects" %f in (*.dwg) do DwgExporter.exe "%f"

# PowerShell batch conversion
Get-ChildItem "C:\Projects\*.dwg" -Recurse | ForEach-Object {
    & "C:\DDC\DwgExporter.exe" $_.FullName
}

Python Integration

python
import subprocess
import pandas as pd
from pathlib import Path
from typing import List, Optional, Dict, Any
from dataclasses import dataclass
from enum import Enum


class DWGEntityType(Enum):
    """DWG entity types."""
    LINE = "LINE"
    POLYLINE = "POLYLINE"
    LWPOLYLINE = "LWPOLYLINE"
    CIRCLE = "CIRCLE"
    ARC = "ARC"
    ELLIPSE = "ELLIPSE"
    SPLINE = "SPLINE"
    TEXT = "TEXT"
    MTEXT = "MTEXT"
    DIMENSION = "DIMENSION"
    INSERT = "INSERT"  # Block reference
    HATCH = "HATCH"
    SOLID = "SOLID"
    POINT = "POINT"
    ATTRIB = "ATTRIB"
    ATTDEF = "ATTDEF"


@dataclass
class DWGEntity:
    """Represents a DWG entity."""
    handle: str
    entity_type: str
    layer: str
    color: int
    linetype: str
    lineweight: float

    # Geometry (depends on entity type)
    start_x: Optional[float] = None
    start_y: Optional[float] = None
    end_x: Optional[float] = None
    end_y: Optional[float] = None

    # Block reference data
    block_name: Optional[str] = None
    rotation: Optional[float] = None
    scale_x: Optional[float] = None
    scale_y: Optional[float] = None

    # Text data
    text_content: Optional[str] = None
    text_height: Optional[float] = None


@dataclass
class DWGBlock:
    """Represents a DWG block definition."""
    name: str
    base_point_x: float
    base_point_y: float
    entity_count: int
    is_dynamic: bool
    attributes: List[str]


@dataclass
class DWGLayer:
    """Represents a DWG layer."""
    name: str
    color: int
    linetype: str
    is_on: bool
    is_frozen: bool
    is_locked: bool
    lineweight: float
    entity_count: int


class DWGExporter:
    """DWG to Excel converter using DDC DwgExporter CLI."""

    def __init__(self, exporter_path: str = "DwgExporter.exe"):
        self.exporter = Path(exporter_path)
        if not self.exporter.exists():
            raise FileNotFoundError(f"DwgExporter not found: {exporter_path}")

    def convert(self, dwg_file: str,
                export_pdf: bool = False) -> Path:
        """Convert DWG file to Excel."""
        dwg_path = Path(dwg_file)
        if not dwg_path.exists():
            raise FileNotFoundError(f"DWG file not found: {dwg_file}")

        cmd = [str(self.exporter), str(dwg_path)]
        if export_pdf:
            cmd.append("sheets2pdf")

        result = subprocess.run(cmd, capture_output=True, text=True)

        if result.returncode != 0:
            raise RuntimeError(f"Export failed: {result.stderr}")

        # Output file is same name with .xlsx extension
        return dwg_path.with_suffix('.xlsx')

    def batch_convert(self, folder: str,
                      include_subfolders: bool = True,
                      export_pdf: bool = False) -> List[Dict[str, Any]]:
        """Convert all DWG files in folder."""
        folder_path = Path(folder)
        pattern = "**/*.dwg" if include_subfolders else "*.dwg"

        results = []
        for dwg_file in folder_path.glob(pattern):
            try:
                output = self.convert(str(dwg_file), export_pdf)
                results.append({
                    'input': str(dwg_file),
                    'output': str(output),
                    'status': 'success'
                })
                print(f"✓ Converted: {dwg_file.name}")
            except Exception as e:
                results.append({
                    'input': str(dwg_file),
                    'output': None,
                    'status': 'failed',
                    'error': str(e)
                })
                print(f"✗ Failed: {dwg_file.name} - {e}")

        return results

    def read_entities(self, xlsx_file: str) -> pd.DataFrame:
        """Read converted Excel as DataFrame."""
        xlsx_path = Path(xlsx_file)
        if not xlsx_path.exists():
            raise FileNotFoundError(f"Excel file not found: {xlsx_file}")

        return pd.read_excel(xlsx_file, sheet_name="Elements")

    def get_layers(self, xlsx_file: str) -> pd.DataFrame:
        """Get layer summary from converted file."""
        df = self.read_entities(xlsx_file)

        if 'Layer' not in df.columns:
            raise ValueError("Layer column not found in data")

        summary = df.groupby('Layer').agg({
            'Handle': 'count'
        }).reset_index()
        summary.columns = ['Layer', 'Entity_Count']
        return summary.sort_values('Entity_Count', ascending=False)

    def get_blocks(self, xlsx_file: str) -> pd.DataFrame:
        """Get block reference summary."""
        df = self.read_entities(xlsx_file)

        # Filter to INSERT entities (block references)
        blocks = df[df['EntityType'] == 'INSERT']

        if blocks.empty:
            return pd.DataFrame(columns=['Block_Name', 'Count'])

        summary = blocks.groupby('BlockName').agg({
            'Handle': 'count'
        }).reset_index()
        summary.columns = ['Block_Name', 'Count']
        return summary.sort_values('Count', ascending=False)

    def get_text_content(self, xlsx_file: str) -> pd.DataFrame:
        """Extract all text content from DWG."""
        df = self.read_entities(xlsx_file)

        # Filter to text entities
        text_types = ['TEXT', 'MTEXT', 'ATTRIB']
        texts = df[df['EntityType'].isin(text_types)]

        if 'TextContent' in texts.columns:
            return texts[['Handle', 'EntityType', 'Layer', 'TextContent']].copy()
        return texts[['Handle', 'EntityType', 'Layer']].copy()

    def get_entity_statistics(self, xlsx_file: str) -> Dict[str, int]:
        """Get entity type statistics."""
        df = self.read_entities(xlsx_file)

        if 'EntityType' not in df.columns:
            return {}

        return df['EntityType'].value_counts().to_dict()

    def extract_block_attributes(self, xlsx_file: str,
                                  block_name: str) -> pd.DataFrame:
        """Extract attributes from specific block type."""
        df = self.read_entities(xlsx_file)

        # Find block references
        blocks = df[(df['EntityType'] == 'INSERT') &
                    (df['BlockName'] == block_name)]

        # Find associated attributes
        # Attributes typically follow their parent INSERT in handle order
        result_data = []

        for _, block in blocks.iterrows():
            block_handle = block['Handle']
            block_data = {
                'Block_Handle': block_handle,
                'X': block.get('InsertX', 0),
                'Y': block.get('InsertY', 0),
                'Rotation': block.get('Rotation', 0)
            }

            # Add any attribute columns
            for col in df.columns:
                if col.startswith('Attr_'):
                    block_data[col] = block.get(col)

            result_data.append(block_data)

        return pd.DataFrame(result_data)


class DWGAnalyzer:
    """Advanced DWG analysis tools."""

    def __init__(self, exporter: DWGExporter):
        self.exporter = exporter

    def analyze_drawing_structure(self, dwg_file: str) -> Dict[str, Any]:
        """Analyze complete drawing structure."""
        xlsx = self.exporter.convert(dwg_file)
        df = self.exporter.read_entities(str(xlsx))

        analysis = {
            'file': dwg_file,
            'total_entities': len(df),
            'layers': self.exporter.get_layers(str(xlsx)).to_dict('records'),
            'entity_types': self.exporter.get_entity_statistics(str(xlsx)),
            'blocks': self.exporter.get_blocks(str(xlsx)).to_dict('records')
        }

        # Calculate extents if coordinates available
        if 'X' in df.columns and 'Y' in df.columns:
            analysis['extents'] = {
                'min_x': df['X'].min(),
                'max_x': df['X'].max(),
                'min_y': df['Y'].min(),
                'max_y': df['Y'].max()
            }

        return analysis

    def compare_drawings(self, dwg1: str, dwg2: str) -> Dict[str, Any]:
        """Compare two DWG files."""
        xlsx1 = self.exporter.convert(dwg1)
        xlsx2 = self.exporter.convert(dwg2)

        df1 = self.exporter.read_entities(str(xlsx1))
        df2 = self.exporter.read_entities(str(xlsx2))

        layers1 = set(df1['Layer'].unique()) if 'Layer' in df1.columns else set()
        layers2 = set(df2['Layer'].unique()) if 'Layer' in df2.columns else set()

        return {
            'file1': dwg1,
            'file2': dwg2,
            'entity_count_diff': len(df2) - len(df1),
            'layers_added': list(layers2 - layers1),
            'layers_removed': list(layers1 - layers2),
            'common_layers': list(layers1 & layers2)
        }

    def find_duplicates(self, xlsx_file: str,
                        tolerance: float = 0.001) -> pd.DataFrame:
        """Find duplicate entities at same location."""
        df = self.exporter.read_entities(xlsx_file)

        if 'X' not in df.columns or 'Y' not in df.columns:
            return pd.DataFrame()

        # Round coordinates for grouping
        df['X_rounded'] = (df['X'] / tolerance).round() * tolerance
        df['Y_rounded'] = (df['Y'] / tolerance).round() * tolerance

        # Find duplicates
        duplicates = df[df.duplicated(
            subset=['EntityType', 'Layer', 'X_rounded', 'Y_rounded'],
            keep=False
        )]

        return duplicates.sort_values(['X_rounded', 'Y_rounded'])


# Convenience functions
def convert_dwg_to_excel(dwg_file: str,
                         exporter_path: str = "DwgExporter.exe") -> str:
    """Quick conversion of DWG to Excel."""
    exporter = DWGExporter(exporter_path)
    output = exporter.convert(dwg_file)
    return str(output)


def batch_convert_dwg(folder: str,
                      exporter_path: str = "DwgExporter.exe",
                      include_subfolders: bool = True) -> List[str]:
    """Batch convert all DWG files in folder."""
    exporter = DWGExporter(exporter_path)
    results = exporter.batch_convert(folder, include_subfolders)
    return [r['output'] for r in results if r['status'] == 'success']

Output Structure

Excel Sheets

SheetContent
ElementsAll DWG entities with properties
LayersLayer definitions
BlocksBlock definitions
LayoutsDrawing layouts/sheets

Entity Columns

ColumnTypeDescription
HandlestringUnique entity handle
EntityTypestringLINE, CIRCLE, INSERT, etc.
LayerstringLayer name
ColorintColor index (0-256)
LinetypestringLinetype name
LineweightfloatLine weight in mm
X, Y, ZfloatEntity coordinates
BlockNamestringFor INSERT entities
TextContentstringFor TEXT/MTEXT

Quick Start

python
# Initialize exporter
exporter = DWGExporter("C:/DDC/DwgExporter.exe")

# Convert single file
xlsx = exporter.convert("C:/Projects/Plan.dwg")
print(f"Output: {xlsx}")

# Read and analyze
df = exporter.read_entities(str(xlsx))
print(f"Total entities: {len(df)}")

# Get layer statistics
layers = exporter.get_layers(str(xlsx))
print(layers)

# Get block usage
blocks = exporter.get_blocks(str(xlsx))
print(blocks)

# Extract text annotations
texts = exporter.get_text_content(str(xlsx))
for _, row in texts.iterrows():
    print(f"{row['Layer']}: {row.get('TextContent', 'N/A')}")

Common Use Cases

1. Layer Audit

python
exporter = DWGExporter()
xlsx = exporter.convert("drawing.dwg")
layers = exporter.get_layers(str(xlsx))

# Check for non-standard layers
standard_layers = ['0', 'WALLS', 'DOORS', 'WINDOWS', 'DIMENSIONS']
non_standard = layers[~layers['Layer'].isin(standard_layers)]
print("Non-standard layers:", non_standard['Layer'].tolist())

2. Block Schedule

python
# Extract all door blocks with attributes
doors = exporter.extract_block_attributes(str(xlsx), "DOOR")
print(doors[['Block_Handle', 'Attr_DOOR_TYPE', 'Attr_DOOR_SIZE']])

3. Drawing Comparison

python
analyzer = DWGAnalyzer(exporter)
diff = analyzer.compare_drawings("rev1.dwg", "rev2.dwg")
print(f"Entities added: {diff['entity_count_diff']}")
print(f"New layers: {diff['layers_added']}")

Integration with DDC Pipeline

python
# Full pipeline: DWG → Excel → Analysis → Report
from dwg_exporter import DWGExporter, DWGAnalyzer

# 1. Convert DWG
exporter = DWGExporter("C:/DDC/DwgExporter.exe")
xlsx = exporter.convert("project.dwg")

# 2. Analyze structure
analyzer = DWGAnalyzer(exporter)
analysis = analyzer.analyze_drawing_structure("project.dwg")

# 3. Generate report
print(f"Drawing: {analysis['file']}")
print(f"Entities: {analysis['total_entities']}")
print(f"Layers: {len(analysis['layers'])}")
print(f"Blocks: {len(analysis['blocks'])}")

Best Practices

  1. Check DWG version - Older files may have limited data
  2. Validate layer structure - Clean up before processing
  3. Handle external references - Bind xrefs if needed
  4. Batch overnight - Large files take time
  5. Verify entity counts - Compare with AutoCAD if possible

Resources

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Dwg To Excel AI skill do?

Convert AutoCAD DWG files (1983-2026) to Excel databases using DwgExporter CLI. Extract layers, blocks, attributes, and geometry data without Autodesk licenses.

Why use Dwg To Excel on TypingMind?

Because you install it once and use it with any model. Dwg To Excel is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Dwg To Excel in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/1_DDC_Toolkit/CAD-Converters/dwg-to-excel. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Dwg To Excel?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Dwg To Excel?

As many as you like. As long as a model supports skills, you can use Dwg To Excel with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Dwg To Excel AI skill free?

Yes. It is published on GitHub by datadrivenconstruction under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇