Batch Cad Converter logo

Batch Cad Converter

Community
datadrivenconstruction
batch-cad-converter

Batch convert multiple CAD/BIM files (Revit, IFC, DWG, DGN) with progress tracking, error handling, and consolidated reporting.

Overview

Publisherdatadrivenconstruction
RepositoryDDC_Skills_for_AI_Agents_in_Construction
Skill namebatch-cad-converter
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 Batch Cad Converter 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/batch-cad-converter
  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/batch-cad-converter .claude/skills/batch-cad-converter
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Batch Cad Converter 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 Batch Cad Converter 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 Batch Cad Converter 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.

Batch CAD/BIM Converter

Business Case

Problem Statement

Large projects and archives contain hundreds or thousands of CAD/BIM files:

  • Manual conversion is tedious and error-prone
  • Different formats require different converters
  • Progress tracking is needed for long operations
  • Error handling is critical for large batches

Solution

Unified batch converter handling all supported formats with progress tracking, error recovery, and consolidated reporting.

Business Value

  • Multi-format - Revit, IFC, DWG, DGN in one workflow
  • Error recovery - Continue on failures
  • Progress tracking - Monitor large batches
  • Reporting - Consolidated conversion results

Python Implementation

python
import subprocess
from pathlib import Path
from typing import List, Optional, Dict, Any, Callable
from dataclasses import dataclass, field
from datetime import datetime
import time
import json
from enum import Enum
from concurrent.futures import ThreadPoolExecutor, as_completed


class CADFormat(Enum):
    """Supported CAD/BIM formats."""
    REVIT = (".rvt", ".rfa")
    IFC = (".ifc",)
    DWG = (".dwg",)
    DGN = (".dgn",)


class ConversionStatus(Enum):
    """Status of conversion operation."""
    PENDING = "pending"
    CONVERTING = "converting"
    SUCCESS = "success"
    FAILED = "failed"
    SKIPPED = "skipped"


@dataclass
class ConversionResult:
    """Result of single file conversion."""
    input_file: str
    output_file: Optional[str]
    format: str
    status: ConversionStatus
    start_time: datetime
    end_time: Optional[datetime]
    duration_seconds: float
    error_message: Optional[str] = None
    file_size_kb: float = 0


@dataclass
class BatchResult:
    """Result of batch conversion."""
    total_files: int
    successful: int
    failed: int
    skipped: int
    total_duration: float
    results: List[ConversionResult]
    start_time: datetime
    end_time: datetime


class BatchCADConverter:
    """Batch convert multiple CAD/BIM files."""

    # Default converter paths
    DEFAULT_CONVERTERS = {
        'revit': 'RvtExporter.exe',
        'ifc': 'IfcExporter.exe',
        'dwg': 'DwgExporter.exe',
        'dgn': 'DgnExporter.exe'
    }

    def __init__(self, converter_dir: str = ".",
                 converters: Dict[str, str] = None):
        self.converter_dir = Path(converter_dir)
        self.converters = converters or self.DEFAULT_CONVERTERS
        self.results: List[ConversionResult] = []
        self.progress_callback: Optional[Callable] = None

    def set_progress_callback(self, callback: Callable[[int, int, str], None]):
        """Set callback for progress updates."""
        self.progress_callback = callback

    def _get_format(self, file_path: Path) -> Optional[str]:
        """Detect CAD format from extension."""
        ext = file_path.suffix.lower()

        for format_name, extensions in [
            ('revit', ('.rvt', '.rfa')),
            ('ifc', ('.ifc',)),
            ('dwg', ('.dwg',)),
            ('dgn', ('.dgn',))
        ]:
            if ext in extensions:
                return format_name

        return None

    def _get_converter(self, format_name: str) -> Optional[Path]:
        """Get converter path for format."""
        if format_name not in self.converters:
            return None

        converter = self.converter_dir / self.converters[format_name]
        if converter.exists():
            return converter

        # Try in system PATH
        return Path(self.converters[format_name])

    def convert_file(self, input_file: str,
                     output_dir: Optional[str] = None,
                     options: List[str] = None) -> ConversionResult:
        """Convert single file."""

        input_path = Path(input_file)
        start_time = datetime.now()

        # Detect format
        format_name = self._get_format(input_path)
        if not format_name:
            return ConversionResult(
                input_file=input_file,
                output_file=None,
                format='unknown',
                status=ConversionStatus.SKIPPED,
                start_time=start_time,
                end_time=datetime.now(),
                duration_seconds=0,
                error_message="Unsupported format"
            )

        # Get converter
        converter = self._get_converter(format_name)
        if not converter:
            return ConversionResult(
                input_file=input_file,
                output_file=None,
                format=format_name,
                status=ConversionStatus.FAILED,
                start_time=start_time,
                end_time=datetime.now(),
                duration_seconds=0,
                error_message=f"Converter not found for {format_name}"
            )

        # Build command
        cmd = [str(converter), str(input_path)]
        if options:
            cmd.extend(options)

        # Execute
        try:
            result = subprocess.run(cmd, capture_output=True, text=True, timeout=3600)
            end_time = datetime.now()
            duration = (end_time - start_time).total_seconds()

            # Determine output file
            output_file = input_path.with_suffix('.xlsx')
            if output_dir:
                output_file = Path(output_dir) / output_file.name

            if result.returncode == 0 and output_file.exists():
                return ConversionResult(
                    input_file=input_file,
                    output_file=str(output_file),
                    format=format_name,
                    status=ConversionStatus.SUCCESS,
                    start_time=start_time,
                    end_time=end_time,
                    duration_seconds=duration,
                    file_size_kb=output_file.stat().st_size / 1024
                )
            else:
                return ConversionResult(
                    input_file=input_file,
                    output_file=None,
                    format=format_name,
                    status=ConversionStatus.FAILED,
                    start_time=start_time,
                    end_time=end_time,
                    duration_seconds=duration,
                    error_message=result.stderr or "Conversion failed"
                )

        except subprocess.TimeoutExpired:
            return ConversionResult(
                input_file=input_file,
                output_file=None,
                format=format_name,
                status=ConversionStatus.FAILED,
                start_time=start_time,
                end_time=datetime.now(),
                duration_seconds=3600,
                error_message="Timeout exceeded (1 hour)"
            )

        except Exception as e:
            return ConversionResult(
                input_file=input_file,
                output_file=None,
                format=format_name,
                status=ConversionStatus.FAILED,
                start_time=start_time,
                end_time=datetime.now(),
                duration_seconds=0,
                error_message=str(e)
            )

    def batch_convert(self, input_folder: str,
                      output_folder: Optional[str] = None,
                      include_subfolders: bool = True,
                      formats: List[str] = None,
                      options: Dict[str, List[str]] = None,
                      parallel: bool = False,
                      max_workers: int = 4) -> BatchResult:
        """Convert all files in folder."""

        start_time = datetime.now()
        input_path = Path(input_folder)

        # Find all supported files
        files = []
        pattern = "**/*" if include_subfolders else "*"

        for ext in ['.rvt', '.rfa', '.ifc', '.dwg', '.dgn']:
            files.extend(input_path.glob(f"{pattern}{ext}"))

        # Filter by format if specified
        if formats:
            files = [f for f in files if self._get_format(f) in formats]

        total_files = len(files)
        self.results = []

        # Create output directory
        if output_folder:
            Path(output_folder).mkdir(parents=True, exist_ok=True)

        # Process files
        if parallel and total_files > 1:
            self._convert_parallel(files, output_folder, options, max_workers)
        else:
            self._convert_sequential(files, output_folder, options)

        end_time = datetime.now()

        # Calculate statistics
        successful = sum(1 for r in self.results if r.status == ConversionStatus.SUCCESS)
        failed = sum(1 for r in self.results if r.status == ConversionStatus.FAILED)
        skipped = sum(1 for r in self.results if r.status == ConversionStatus.SKIPPED)

        return BatchResult(
            total_files=total_files,
            successful=successful,
            failed=failed,
            skipped=skipped,
            total_duration=(end_time - start_time).total_seconds(),
            results=self.results,
            start_time=start_time,
            end_time=end_time
        )

    def _convert_sequential(self, files: List[Path],
                            output_folder: Optional[str],
                            options: Dict[str, List[str]]):
        """Convert files sequentially."""

        total = len(files)
        for i, file_path in enumerate(files, 1):
            if self.progress_callback:
                self.progress_callback(i, total, str(file_path))

            format_name = self._get_format(file_path)
            format_options = options.get(format_name, []) if options else []

            result = self.convert_file(str(file_path), output_folder, format_options)
            self.results.append(result)

            status_symbol = "✓" if result.status == ConversionStatus.SUCCESS else "✗"
            print(f"[{i}/{total}] {status_symbol} {file_path.name}")

    def _convert_parallel(self, files: List[Path],
                          output_folder: Optional[str],
                          options: Dict[str, List[str]],
                          max_workers: int):
        """Convert files in parallel."""

        total = len(files)

        with ThreadPoolExecutor(max_workers=max_workers) as executor:
            futures = {}

            for file_path in files:
                format_name = self._get_format(file_path)
                format_options = options.get(format_name, []) if options else []
                future = executor.submit(self.convert_file, str(file_path), output_folder, format_options)
                futures[future] = file_path

            completed = 0
            for future in as_completed(futures):
                completed += 1
                result = future.result()
                self.results.append(result)

                if self.progress_callback:
                    self.progress_callback(completed, total, str(futures[future]))

    def generate_report(self, batch_result: BatchResult,
                        output_path: str = None) -> str:
        """Generate conversion report."""

        report = {
            'summary': {
                'total_files': batch_result.total_files,
                'successful': batch_result.successful,
                'failed': batch_result.failed,
                'skipped': batch_result.skipped,
                'success_rate': round(batch_result.successful / batch_result.total_files * 100, 1) if batch_result.total_files > 0 else 0,
                'total_duration_seconds': round(batch_result.total_duration, 2),
                'start_time': batch_result.start_time.isoformat(),
                'end_time': batch_result.end_time.isoformat()
            },
            'results': [
                {
                    'input': r.input_file,
                    'output': r.output_file,
                    'format': r.format,
                    'status': r.status.value,
                    'duration': round(r.duration_seconds, 2),
                    'error': r.error_message
                }
                for r in batch_result.results
            ]
        }

        report_json = json.dumps(report, indent=2)

        if output_path:
            with open(output_path, 'w') as f:
                f.write(report_json)

        return report_json


# Progress callback example
def print_progress(current: int, total: int, file_name: str):
    """Print progress to console."""
    percent = current / total * 100
    print(f"Progress: {current}/{total} ({percent:.1f}%) - {file_name}")

Quick Start

python
# Initialize batch converter
converter = BatchCADConverter(converter_dir="C:/DDC/")

# Set progress callback
converter.set_progress_callback(print_progress)

# Convert all files
result = converter.batch_convert(
    input_folder="C:/Projects",
    output_folder="C:/Converted",
    include_subfolders=True
)

print(f"Success: {result.successful}/{result.total_files}")

Common Use Cases

1. Convert Specific Formats Only

python
result = converter.batch_convert(
    input_folder="C:/Archive",
    formats=['revit', 'ifc'],  # Only Revit and IFC
    parallel=True,
    max_workers=4
)

2. With Format-Specific Options

python
options = {
    'revit': ['complete', 'bbox', 'rooms'],
    'ifc': ['bbox'],
    'dwg': []
}
result = converter.batch_convert(
    input_folder="C:/Projects",
    options=options
)

3. Generate Report

python
result = converter.batch_convert("C:/Projects")
report = converter.generate_report(result, "conversion_report.json")

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 Batch Cad Converter AI skill do?

Batch convert multiple CAD/BIM files (Revit, IFC, DWG, DGN) with progress tracking, error handling, and consolidated reporting.

Why use Batch Cad Converter on TypingMind?

Because you install it once and use it with any model. Batch Cad Converter 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 Batch Cad Converter 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/batch-cad-converter. 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 Batch Cad Converter?

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 Batch Cad Converter?

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

Is the Batch Cad Converter 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 👇