Sandbox Environment

Introduction

A versatile and secure sandbox environment designed for experimentation, testing, and development. This multi-purpose repository serves as a playground for exploring new technologies.

The 'SandboxEnvironment' provides a structured space for safely testing APIs, tools, and workflows without impacting production systems. It integrates modern DevOps practices — including automated CI/CD pipelines, dependency management, and supply-chain security scanning — to demonstrate best practices in repository maintenance.

Key areas covered in this sandbox include:

• AI-assisted development — integration with large language model APIs such as Claude and LM Studio for development and testing workflows.
• Security tooling — CodeQL static analysis, OpenSSF Scorecard, Dependabot automated dependency updates, and secret scanning via pre-commit hooks.
• Cross-platform sandboxing — references and resources for Linux, macOS, and Windows sandbox environments.
• Open research — archiving and DOI minting via Zenodo to make outputs citable and accessible.

Getting Started

Prerequisites

• Python 3.10 or higher
• Git
• Docker (optional, for containerized development)

Installation (Optional)

1. Clone the repository:

   git clone https://github.com/Git-Hub-Chris/SandboxEnvironment.git
   cd SandboxEnvironment

2. Create a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt
   # Or for development with extra tools:
   pip install -e ".[dev]"

Running Tests

Run the test suite with pytest:

pytest -v

Run tests with coverage:

pytest --cov=. --cov-report=term-missing

Code Quality

Check code formatting with Black:

black --check .
# Or format automatically:
black .

Run linting with Ruff:

ruff check .

Docker Usage

Build and run tests in Docker:

docker-compose run test

Interactive development environment:

docker-compose run dev

For more Docker details, see DOCKER.md.

Configuration

The repository includes a Phi-4.prompt.yml configuration file for testing with Microsoft Phi-4 language model integration, providing model parameters and system prompts for AI-assisted development workflows.

Software testing

Claude API

https://platform.claude.com/docs/en/api/overview

LM Studio API

https://lmstudio.ai/docs/developer/rest

Visual Studio Code API

https://code.visualstudio.com/api/references/vscode-api

ORCID Sandbox

https://sandbox.orcid.org/

Local Contexts Hub Sandbox

https://sandbox.localcontextshub.org/dashboard/

Linux Sandbox

https://labex.io/tutorials/linux-online-linux-terminal-and-playground-372915

MacOS Sandbox

https://developer.apple.com/documentation/xcode/configuring-the-macos-app-sandbox

Windows Sandbox

https://learn.microsoft.com/en-us/windows/security/application-security/application-isolation/windows-sandbox/

Acknowledgements

A special thank you to these organizations for providing the digital resources necessary to help create this repository. Enjoy the sandbox.

• GitHub — repository hosting, GitHub Actions CI/CD, Dependabot, CodeQL security scanning, and GitHub Copilot.
• Zenodo — open-access archiving and DOI minting for research outputs.
• OpenSSF — Open Source Security Foundation Best Practices badge and Scorecard supply-chain security tooling.
• Anthropic — Claude API for AI-assisted development and testing.
• LM Studio — local large language model inference API for development and testing.
• Local Contexts — Open to Collaborate Notice, supporting Indigenous data sovereignty and ethical collaboration.