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auto-md

Convert files, folders, and GitHub repositories into AI/LLM-ready files.

Overview​

auto-md is a powerful Python-based tool designed to streamline the conversion of files and folders into structured Markdown files, making them ready for AI and large language model (LLM) processing. Whether you’re curating datasets for AI training or organizing project documentation, auto-md provides a fast and efficient way to prepare your data.

Key Features​

  • Markdown Conversion: Transforms files and directories into Markdown format for compatibility with AI/LLM workflows.
  • GitHub Repository Support: Pulls and converts repositories directly into readable Markdown files.
  • Batch Processing: Handles multiple files and folders simultaneously, saving time for large projects.
  • Customizable Output: Allows users to define output structure and formatting preferences.
  • Lightweight and Fast: Optimized for quick conversions with minimal resource usage.

Use Cases​

  • AI/LLM Dataset Preparation: Quickly format text and data for machine learning pipelines.
  • Documentation Consolidation: Convert scattered project files into a unified Markdown format for easy sharing.
  • Content Curation: Extract and format useful content from GitHub repositories or local directories.
  • Data Transformation: Reorganize large datasets into structured files that are easy to parse and process.

Features in Action​

1. File and Folder Conversion​

Convert local files and directories into Markdown files while preserving structure:

  • Extracts content from text files.
  • Supports a variety of file types for seamless transformation.

2. GitHub Repository Integration​

Fetch and convert entire GitHub repositories into AI-ready Markdown format:

  • Crawls README files, directories, and additional documentation.
  • Ideal for preparing datasets from open-source repositories.

3. Batch Operations​

Process multiple files or directories in one go:

  • Handles extensive datasets with a single command.
  • Generates organized output folders with minimal configuration.

Advanced Options​

  • Custom Formatting: Define headers, footers, and content separators for Markdown output.
  • Selective Conversion: Include or exclude specific file types, directories, or subfolders during processing.
  • Logging: View detailed logs to track processing steps and handle large-scale transformations with confidence.

Benefits​

  • Time-Saving: Automates tedious file formatting tasks.
  • Versatile: Works for developers, researchers, and content creators alike.
  • Scalable: Suitable for small projects and large datasets with equal efficiency.
  • Open Source: Built for collaboration and continuous improvement.

Example Scenarios​

  1. Preparing a Research Dataset Transform research notes and datasets into well-structured Markdown files for use in machine learning or natural language processing workflows.

  2. Converting a GitHub Repository Extract and convert content from an open-source project to create a Markdown-based summary or documentation hub.

  3. Organizing Documentation Gather and restructure internal documentation for better organization and readability.

Future Roadmap​

  • Enhanced Format Support: Add support for more file types like PDFs and spreadsheets.
  • Cloud Integration: Enable direct access to cloud storage for input and output files.
  • Interactive Web App: Build a user-friendly interface for non-technical users.
  • GitHub Repository: auto-md on GitHub
  • Documentation: Explore detailed examples and advanced usage.
  • Community Support: Join discussions and contribute to the project.