foundry-instance-manager 0.1.26

A command-line tool for managing Foundry VTT instances

Foundry Instance Manager

A CLI tool for managing multiple Docker containers that share the same image but have individual data directories and a shared data directory.

Features

• Create and manage multiple Docker containers from the same image
• Each container has its own isolated data directory
• Shared data directory accessible by all containers
• Easy-to-use CLI interface
• Container status monitoring
• Automated changelog generation
• Automated version bumping and release workflow
• Security checks (Bandit, Safety)

Installation

1. Clone this repository
2. Install Poetry
3. Install dependencies:

poetry install

Usage

# Create a new container
poetry run fim create --name my-container --image my-image

# List all containers
poetry run fim list

# Start a container
poetry run fim start --name my-container

# Stop a container
poetry run fim stop --name my-container

# Remove a container
poetry run fim remove --name my-container

Configuration

The tool uses the following directory structure:

• ./data/shared/ - Shared data directory accessible by all containers
• ./data/containers/<container-name>/ - Individual container data directories

Requirements

• Python 3.11+
• Docker installed and running
• Poetry

CI/CD Pipeline (GitHub Actions)

This project uses a robust CI/CD pipeline with the following stages:

• Quality Checks: Linting (flake8, black, isort), type checking (mypy), and unit tests with coverage (pytest, pytest-cov). Coverage is uploaded to Codecov.
• Security: Bandit for static security analysis and Safety for dependency vulnerability checks.
• Version Management: Automated version bumping and changelog generation on every push to main.
• Release: Builds the package, creates a GitHub release, and publishes to PyPI when a new tag is created.

How it works

• On every push or pull request to main, the pipeline runs quality and security checks.
• On pushes to main, if all checks pass, the version is bumped, a changelog is generated, and a new tag is created.
• When a tag is pushed, a release is created and the package is published to PyPI.

Changelog Generation

Changelog is automatically generated using the changelog tool. To manually generate or update the changelog:

poetry run changelog generate-md CHANGELOG.md

Security

• Run Bandit:

  poetry run bandit -r foundry_instance_manager
  

• Run Safety:

  poetry run safety check
  

Development with Cursor

Cursor is an AI-powered IDE that can help with development tasks. Here are some useful prompts you can use:

Code Quality

• "Run flake8. For each issue found, understand the issue and surrounding code, explain the issue and your proposed solution, and finally implement your solution. Then rerun flake8 to verify the issue has been fixed. Repeat until all issues are fixed."
• "Run mypy. For each type error found, understand the issue and surrounding code, explain the issue and your proposed solution, and finally implement your solution. Then rerun mypy to verify the issue has been fixed. Repeat until all issues are fixed."
• "Run pytest. For each failing test, understand the failure and surrounding code, explain the issue and your proposed solution, and finally implement your solution. Then rerun pytest to verify the test passes. Repeat until all tests pass."

Code Generation

• "Generate unit tests for the following function/class: [function/class name]"
• "Add docstrings to all public methods in [file name]"
• "Add type hints to all functions in [file name]"

Code Review

• "Review the changes in [file name] and suggest improvements for code quality, performance, and maintainability"
• "Check for potential security issues in [file name]"
• "Suggest ways to improve error handling in [file name]"

Documentation

• "Update the README.md with documentation for [feature/functionality]"
• "Generate API documentation for [module/class]"
• "Add inline comments to explain complex logic in [file name]"

Refactoring

• "Refactor [function/class] to improve readability and maintainability"
• "Extract common functionality into a shared utility module"
• "Split [large function/class] into smaller, more focused components"

Remember to be specific in your prompts and provide context when needed. Cursor works best when given clear instructions and relevant information about the task at hand.