mist.dclean 0.1.0

A tool for analyzing Dockerfiles and Docker images

🐳 DClean

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Table of Contents

• Project Description
• Installation
• Usage
• Project Structure
• Technologies
• Authors
• Contributing

Description

DClean is a powerful tool for analyzing Docker images and Dockerfiles. DClean leverages Trivy for comprehensive vulnerability scanning and provides actionable recommendations to optimize your Dockerfile instructions, helping you create leaner, faster, and more secure containers.

Installation

Prerequisites

• Docker installed on your system
• Python 3.10 or higher
• pip (Python package manager)

Option 1: Install from PyPI

pip install dclean

Option 2: Install from Source

git clone https://github.com/mist941/dclean.git

cd dclean

pip install -e .

Verify Installation

dclean version

You should see the following output:

dclean version 0.1.0

Usage

Analyze a Dockerfile:

dclean analyze path/to/Dockerfile

Analysis Options

Save analysis results to a file:

dclean analyze path/to/Dockerfile --output path/to/output.txt

Perform deep analysis (includes vulnerability scanning):

dclean analyze path/to/Dockerfile --deep

Examples

Deep analysis with colored output in terminal:

dclean analyze dockerfiles/Dockerfile --deep

Save results to a file:

dclean analyze dockerfiles/Dockerfile --deep --output examples/file_output.txt

📄 View sample output file

Project Structure

The DClean project is organized as follows:

dclean/
├── main.py # CLI entry point
├── analyze # Analyze Dockerfiles and Docker images
├── utils # Utility functions
├── api # API for analyzing Docker images
└── tests # Tests

Technologies

DClean is built using the following technologies and tools:

• Python 3.10+: Core programming language
• Click: Command-line interface creation
• Docker SDK for Python: Interacting with Docker API
• Trivy: Vulnerability scanning for container images
• pytest: Testing framework
• flake8: Linting

Authors

• Ivan Statkevich - Initial work - mist941

This project is maintained by Ivan Statkevich. Feel free to reach out with questions or feedback.

Contributing

🤝 Contributions are welcome! Feel free to open issues and submit pull requests.

Getting Started

1. Fork the repository on GitHub
2. Clone your fork to your local machine
3. Create a new branch from the dev branch for your feature or bugfix
4. Make your changes and commit them with descriptive messages
5. Push your branch to your GitHub fork
6. Open a pull request from your branch to the dev branch of the main repository

Reporting Issues

If you find a bug or have a feature request, please open an issue on GitHub with:

• A clear, descriptive title
• A detailed description of the issue or feature
• Steps to reproduce (for bugs)
• Any relevant logs or screenshots