# Send Dgn To Excel 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": "dgn-to-excel",
    "name": "Dgn To Excel",
    "source": "tencent",
    "type": "skill",
    "category": "数据分析",
    "sourceUrl": "https://clawhub.ai/datadrivenconstruction/dgn-to-excel",
    "canonicalUrl": "https://clawhub.ai/datadrivenconstruction/dgn-to-excel",
    "targetPlatform": "OpenClaw"
  },
  "install": {
    "downloadUrl": "/downloads/dgn-to-excel",
    "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=dgn-to-excel",
    "sourcePlatform": "tencent",
    "targetPlatform": "OpenClaw",
    "packageFormat": "ZIP package",
    "primaryDoc": "SKILL.md",
    "includedAssets": [
      "claw.json",
      "instructions.md",
      "SKILL.md"
    ],
    "downloadMode": "redirect",
    "sourceHealth": {
      "source": "tencent",
      "status": "healthy",
      "reason": "direct_download_ok",
      "recommendedAction": "download",
      "checkedAt": "2026-04-23T16:43:11.935Z",
      "expiresAt": "2026-04-30T16:43:11.935Z",
      "httpStatus": 200,
      "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard",
      "contentType": "application/zip",
      "probeMethod": "head",
      "details": {
        "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard",
        "contentDisposition": "attachment; filename=\"4claw-imageboard-1.0.1.zip\"",
        "redirectLocation": null,
        "bodySnippet": null
      },
      "scope": "source",
      "summary": "Source download looks usable.",
      "detail": "Yavira can redirect you to the upstream package for this source.",
      "primaryActionLabel": "Download for OpenClaw",
      "primaryActionHref": "/downloads/dgn-to-excel"
    },
    "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/dgn-to-excel",
    "downloadUrl": "https://openagent3.xyz/downloads/dgn-to-excel",
    "agentUrl": "https://openagent3.xyz/skills/dgn-to-excel/agent",
    "manifestUrl": "https://openagent3.xyz/skills/dgn-to-excel/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/dgn-to-excel/agent.md"
  }
}
```
## Documentation

### Problem Statement

DGN files are common in infrastructure and civil engineering:

Transportation and highway design
Bridge and tunnel projects
Utility networks
Rail infrastructure

Extracting structured data from DGN files for analysis and reporting can be challenging.

### Solution

Convert DGN files to structured Excel databases, supporting both v7 and v8 formats.

### Business Value

Infrastructure support - Civil engineering focused
Legacy format support - V7 and V8 DGN files
Data extraction - Levels, cells, text, geometry
Batch processing - Process multiple files
Structured output - Excel format for analysis

### CLI Syntax

DgnExporter.exe <input_dgn>

### Supported Versions

VersionDescriptionV7 DGNLegacy MicroStation format (pre-V8)V8 DGNModern MicroStation formatV8i DGNMicroStation V8i format

### Output Format

OutputDescription.xlsxExcel database with all elements

### Examples

# Basic conversion
DgnExporter.exe "C:\\Projects\\Bridge.dgn"

# Batch processing
for /R "C:\\Infrastructure" %f in (*.dgn) do DgnExporter.exe "%f"

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

### Python Integration

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 DGNElementType(Enum):
    """DGN element types."""
    CELL_HEADER = 2
    LINE = 3
    LINE_STRING = 4
    SHAPE = 6
    TEXT_NODE = 7
    CURVE = 11
    COMPLEX_CHAIN = 12
    COMPLEX_SHAPE = 14
    ELLIPSE = 15
    ARC = 16
    TEXT = 17
    SURFACE = 18
    SOLID = 19
    BSPLINE_CURVE = 21
    POINT_STRING = 22
    DIMENSION = 33
    SHARED_CELL = 35


@dataclass
class DGNElement:
    """Represents a DGN element."""
    element_id: int
    element_type: int
    type_name: str
    level: int
    color: int
    weight: int
    style: int

    # Geometry
    range_low_x: Optional[float] = None
    range_low_y: Optional[float] = None
    range_low_z: Optional[float] = None
    range_high_x: Optional[float] = None
    range_high_y: Optional[float] = None
    range_high_z: Optional[float] = None

    # Cell/Text specific
    cell_name: Optional[str] = None
    text_content: Optional[str] = None


@dataclass
class DGNLevel:
    """Represents a DGN level."""
    number: int
    name: str
    is_displayed: bool
    is_frozen: bool
    element_count: int


class DGNExporter:
    """DGN to Excel converter using DDC DgnExporter CLI."""

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

    def convert(self, dgn_file: str) -> Path:
        """Convert DGN file to Excel."""
        dgn_path = Path(dgn_file)
        if not dgn_path.exists():
            raise FileNotFoundError(f"DGN file not found: {dgn_file}")

        cmd = [str(self.exporter), str(dgn_path)]
        result = subprocess.run(cmd, capture_output=True, text=True)

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

        return dgn_path.with_suffix('.xlsx')

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

        results = []
        for dgn_file in folder_path.glob(pattern):
            try:
                output = self.convert(str(dgn_file))
                results.append({
                    'input': str(dgn_file),
                    'output': str(output),
                    'status': 'success'
                })
                print(f"✓ Converted: {dgn_file.name}")
            except Exception as e:
                results.append({
                    'input': str(dgn_file),
                    'output': None,
                    'status': 'failed',
                    'error': str(e)
                })
                print(f"✗ Failed: {dgn_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_levels(self, xlsx_file: str) -> pd.DataFrame:
        """Get level summary."""
        df = self.read_elements(xlsx_file)

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

        summary = df.groupby('Level').agg({
            'ElementId': 'count'
        }).reset_index()
        summary.columns = ['Level', 'Element_Count']
        return summary.sort_values('Level')

    def get_element_types(self, xlsx_file: str) -> pd.DataFrame:
        """Get element type statistics."""
        df = self.read_elements(xlsx_file)

        type_col = 'ElementType' if 'ElementType' in df.columns else 'Type'
        if type_col not in df.columns:
            return pd.DataFrame()

        summary = df.groupby(type_col).agg({
            'ElementId': 'count'
        }).reset_index()
        summary.columns = ['Element_Type', 'Count']
        return summary.sort_values('Count', ascending=False)

    def get_cells(self, xlsx_file: str) -> pd.DataFrame:
        """Get cell references (similar to blocks in DWG)."""
        df = self.read_elements(xlsx_file)

        # Filter to cell elements
        cells = df[df['ElementType'].isin([2, 35])]  # CELL_HEADER, SHARED_CELL

        if cells.empty or 'CellName' not in cells.columns:
            return pd.DataFrame(columns=['Cell_Name', 'Count'])

        summary = cells.groupby('CellName').agg({
            'ElementId': 'count'
        }).reset_index()
        summary.columns = ['Cell_Name', 'Count']
        return summary.sort_values('Count', ascending=False)

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

        # Filter to text elements
        text_types = [7, 17]  # TEXT_NODE, TEXT
        texts = df[df['ElementType'].isin(text_types)]

        if 'TextContent' in texts.columns:
            return texts[['ElementId', 'Level', 'TextContent']].copy()
        return texts[['ElementId', 'Level']].copy()

    def get_statistics(self, xlsx_file: str) -> Dict[str, Any]:
        """Get comprehensive DGN statistics."""
        df = self.read_elements(xlsx_file)

        stats = {
            'total_elements': len(df),
            'levels_used': df['Level'].nunique() if 'Level' in df.columns else 0,
            'element_types': df['ElementType'].nunique() if 'ElementType' in df.columns else 0
        }

        # Calculate extents
        for coord in ['X', 'Y', 'Z']:
            low_col = f'RangeLow{coord}'
            high_col = f'RangeHigh{coord}'
            if low_col in df.columns and high_col in df.columns:
                stats[f'min_{coord.lower()}'] = df[low_col].min()
                stats[f'max_{coord.lower()}'] = df[high_col].max()

        return stats


class DGNAnalyzer:
    """Advanced DGN analysis for infrastructure projects."""

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

    def analyze_infrastructure(self, dgn_file: str) -> Dict[str, Any]:
        """Analyze DGN for infrastructure elements."""
        xlsx = self.exporter.convert(dgn_file)
        df = self.exporter.read_elements(str(xlsx))

        analysis = {
            'file': dgn_file,
            'statistics': self.exporter.get_statistics(str(xlsx)),
            'levels': self.exporter.get_levels(str(xlsx)).to_dict('records'),
            'element_types': self.exporter.get_element_types(str(xlsx)).to_dict('records'),
            'cells': self.exporter.get_cells(str(xlsx)).to_dict('records')
        }

        # Identify infrastructure-specific elements
        if 'ElementType' in df.columns:
            # Lines and shapes (often roads, boundaries)
            lines = df[df['ElementType'].isin([3, 4, 6, 14])].shape[0]
            analysis['linear_elements'] = lines

            # Complex elements (often structures)
            complex_elements = df[df['ElementType'].isin([12, 14, 18, 19])].shape[0]
            analysis['complex_elements'] = complex_elements

            # Annotation elements
            annotations = df[df['ElementType'].isin([7, 17, 33])].shape[0]
            analysis['annotations'] = annotations

        return analysis

    def compare_revisions(self, dgn1: str, dgn2: str) -> Dict[str, Any]:
        """Compare two DGN revisions."""
        xlsx1 = self.exporter.convert(dgn1)
        xlsx2 = self.exporter.convert(dgn2)

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

        levels1 = set(df1['Level'].unique()) if 'Level' in df1.columns else set()
        levels2 = set(df2['Level'].unique()) if 'Level' in df2.columns else set()

        return {
            'revision1': dgn1,
            'revision2': dgn2,
            'element_count_diff': len(df2) - len(df1),
            'levels_added': list(levels2 - levels1),
            'levels_removed': list(levels1 - levels2),
            'common_levels': len(levels1 & levels2)
        }

    def extract_coordinates(self, xlsx_file: str) -> pd.DataFrame:
        """Extract element coordinates for GIS integration."""
        df = self.exporter.read_elements(xlsx_file)

        coord_cols = ['ElementId', 'Level', 'ElementType']
        for col in ['RangeLowX', 'RangeLowY', 'RangeLowZ',
                    'RangeHighX', 'RangeHighY', 'RangeHighZ',
                    'CenterX', 'CenterY', 'CenterZ']:
            if col in df.columns:
                coord_cols.append(col)

        return df[coord_cols].copy()


class DGNLevelManager:
    """Manage DGN level structures."""

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

    def get_level_map(self, xlsx_file: str) -> Dict[int, str]:
        """Create level number to name mapping."""
        df = self.exporter.read_elements(xlsx_file)

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

        # MicroStation levels are typically numbered 1-63 (V7) or unlimited (V8)
        level_map = {}
        for level in df['Level'].unique():
            level_map[int(level)] = f"Level_{level}"

        return level_map

    def filter_by_levels(self, xlsx_file: str,
                         levels: List[int]) -> pd.DataFrame:
        """Filter elements by level numbers."""
        df = self.exporter.read_elements(xlsx_file)
        return df[df['Level'].isin(levels)]

    def get_level_usage_report(self, xlsx_file: str) -> pd.DataFrame:
        """Generate level usage report."""
        df = self.exporter.read_elements(xlsx_file)

        if 'Level' not in df.columns or 'ElementType' not in df.columns:
            return pd.DataFrame()

        # Cross-tabulate levels and element types
        report = pd.crosstab(df['Level'], df['ElementType'], margins=True)
        return report


# Convenience functions
def convert_dgn_to_excel(dgn_file: str,
                         exporter_path: str = "DgnExporter.exe") -> str:
    """Quick conversion of DGN to Excel."""
    exporter = DGNExporter(exporter_path)
    output = exporter.convert(dgn_file)
    return str(output)


def analyze_dgn(dgn_file: str,
                exporter_path: str = "DgnExporter.exe") -> Dict[str, Any]:
    """Analyze DGN file and return summary."""
    exporter = DGNExporter(exporter_path)
    analyzer = DGNAnalyzer(exporter)
    return analyzer.analyze_infrastructure(dgn_file)

### Excel Sheets

SheetContentElementsAll DGN elements with propertiesLevelsLevel definitionsCellsCell library

### Element Columns

ColumnTypeDescriptionElementIdintUnique element IDElementTypeintType code (3=Line, 17=Text, etc.)LevelintLevel numberColorintColor indexWeightintLine weightStyleintLine styleRangeLowX/Y/ZfloatBounding box minimumRangeHighX/Y/ZfloatBounding box maximumCellNamestringCell name (for cell elements)TextContentstringText content (for text elements)

### Quick Start

# Initialize exporter
exporter = DGNExporter("C:/DDC/DgnExporter.exe")

# Convert DGN to Excel
xlsx = exporter.convert("C:/Projects/Highway.dgn")
print(f"Output: {xlsx}")

# Read elements
df = exporter.read_elements(str(xlsx))
print(f"Total elements: {len(df)}")

# Get level statistics
levels = exporter.get_levels(str(xlsx))
print(levels)

# Get element types
types = exporter.get_element_types(str(xlsx))
print(types)

### 1. Infrastructure Analysis

exporter = DGNExporter()
analyzer = DGNAnalyzer(exporter)

analysis = analyzer.analyze_infrastructure("highway.dgn")
print(f"Total elements: {analysis['statistics']['total_elements']}")
print(f"Linear elements: {analysis['linear_elements']}")
print(f"Annotations: {analysis['annotations']}")

### 2. Level Audit

exporter = DGNExporter()
xlsx = exporter.convert("bridge.dgn")
levels = exporter.get_levels(str(xlsx))

# Check for unused standard levels
for idx, row in levels.iterrows():
    print(f"Level {row['Level']}: {row['Element_Count']} elements")

### 3. GIS Integration

analyzer = DGNAnalyzer(exporter)
xlsx = exporter.convert("utilities.dgn")
coords = analyzer.extract_coordinates(str(xlsx))

# Export for GIS
coords.to_csv("coordinates.csv", index=False)

### 4. Revision Comparison

analyzer = DGNAnalyzer(exporter)
diff = analyzer.compare_revisions("rev1.dgn", "rev2.dgn")
print(f"Elements changed: {diff['element_count_diff']}")

### Integration with DDC Pipeline

# Infrastructure pipeline: DGN → Excel → Analysis
from dgn_exporter import DGNExporter, DGNAnalyzer

# 1. Convert DGN
exporter = DGNExporter("C:/DDC/DgnExporter.exe")
xlsx = exporter.convert("highway_project.dgn")

# 2. Analyze structure
stats = exporter.get_statistics(str(xlsx))
print(f"Elements: {stats['total_elements']}")
print(f"Levels: {stats['levels_used']}")

# 3. Extract for GIS
analyzer = DGNAnalyzer(exporter)
coords = analyzer.extract_coordinates(str(xlsx))
coords.to_csv("for_gis.csv", index=False)

### Best Practices

Check version - V7 and V8 have different capabilities
Reference files - Process all reference files separately
Level mapping - Document level standards for your organization
Coordinate systems - Verify units and coordinate systems
Cell libraries - Export cells separately if needed

### Resources

GitHub: cad2data Pipeline
DDC Book: Chapter 2.4 - CAD Data Extraction
MicroStation: Infrastructure-focused CAD software
## Trust
- Source: tencent
- Verification: Indexed source record
- Publisher: datadrivenconstruction
- Version: 2.0.0
## Source health
- Status: healthy
- Source download looks usable.
- Yavira can redirect you to the upstream package for this source.
- Health scope: source
- Reason: direct_download_ok
- Checked at: 2026-04-23T16:43:11.935Z
- Expires at: 2026-04-30T16:43:11.935Z
- Recommended action: Download for OpenClaw
## Links
- [Detail page](https://openagent3.xyz/skills/dgn-to-excel)
- [Send to Agent page](https://openagent3.xyz/skills/dgn-to-excel/agent)
- [JSON manifest](https://openagent3.xyz/skills/dgn-to-excel/agent.json)
- [Markdown brief](https://openagent3.xyz/skills/dgn-to-excel/agent.md)
- [Download page](https://openagent3.xyz/downloads/dgn-to-excel)