codex-cli-studio 0.1.1

AI-powered CLI tools (explain, script, visualize, config) using OpenAI models.

🧰 Codex CLI Studio

A powerful suite of command-line tools powered by OpenAI models, built to supercharge productivity, learning, and automation for developers, DevOps, and students alike.

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👀 Demo

See Codex CLI Studio in action! This animation shows basic usage of the explain, script, and visualize commands.

(Generated using asciinema and termtosvg)

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🚀 Overview & Status

Codex CLI Studio is a modular set of CLI tools leveraging OpenAI's API.

Current Modules (v0.1.0):

• ✅ explain: Explain code, shell commands, or file content.
• ✅ script: Generate scripts (Bash, Python, PowerShell) from natural language.
• ✅ visualize: Generate function call graphs for Python files (DOT/Image output).
• ✅ config explain: Explain configuration files.
• 🛠️ config edit: Modify configuration files (Planned).

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🔌 Installation

Prerequisites:

• Python 3.9+
• pip
• Graphviz (specifically the dot command) - Required only for rendering visualizations to image formats (png, svg, etc.) using the visualize command.
• An OpenAI API Key.

Install using pip:

pip install codex-cli-studio

Set up your OpenAI API Key: The tool reads the API key from the OPENAI_API_KEY environment variable.

You can set it:

• System-wide: Add export OPENAI_API_KEY='your_key_here' to your shell profile (.zshrc, .bashrc, etc.).
• Per session: Run export OPENAI_API_KEY='your_key_here' in your terminal before using cstudio.
• Using a .env file: Create a .env file in the directory where you run the cstudio command and add the line OPENAI_API_KEY='your_key_here'.

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✨ Usage

After installation, use the cstudio command:

# General help
cstudio --help

# Explain a code snippet
cstudio explain 'import sys; print(sys.argv[1])' --lang en

# Explain a file in detail
cstudio explain path/to/your/code.py --detail detailed

# Generate a Python script
cstudio script "read lines from data.txt and print them numbered" -t python

# Generate a bash script (dry run only)
cstudio script "delete all *.tmp files in /tmp" --dry-run

# Visualize a Python file, saving as PNG
cstudio visualize path/to/visualize.py -f png -o visualize_graph.png

# Explain a YAML config file
cstudio config explain path/to/config.yaml

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🛠️ Module Details

explain ✅

Explains code snippets, shell commands, or file content.

• Supports various languages (auto-detected by AI).
• Options: --detail basic|detailed, --lang <language_code>.

script ✅

Generates executable scripts from natural language tasks.

• Supports: Bash, Python, PowerShell.
• Options: --type <bash|python|powershell>, --dry-run (only displays script).

visualize ✅

Generates function call graphs for Python files.

• Input: Python file (.py).
• Output: Graphviz DOT (.gv, .dot) or rendered image (.png, .svg, .pdf, etc.).
• Requires Graphviz (dot command) for image rendering.
• Options: --output <path>, --format <format>.

config explain ✅

Explains various configuration files (YAML, INI, Dockerfile, etc.).

• Input: Path to configuration file.

config edit 🛠️ (Planned)

Modify configuration files using natural language instructions.

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🌍 Why It Matters

• ✅ Educational: Quickly understand unfamiliar code, commands, or configs.
• ⚡ Productive: Automate script generation and explanations, reducing boilerplate and search time.
• 🔧 Extensible: Modular design allows for adding new commands and capabilities.
• 🌱 Open Source: Community contributions are welcome!

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📦 Built With

• Python 3.9+
• OpenAI API (gpt-4o or other models)
• Typer - for building the CLI interface.
• Rich - for beautiful terminal output.
• python-dotenv - for managing environment variables.
• Graphviz (Python library) - for generating DOT graph descriptions.
• Standard Python libraries (ast, subprocess, shutil, etc.)

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🔮 Roadmap / Coming Soon

• Testing: Increase test coverage, especially for edge cases and options.
• Error Handling: Improve robustness and user feedback on errors.
• config edit: Implement the configuration editing functionality.
• visualize Enhancements: Options to exclude nodes (builtins), different layouts.
• More Modules? test generation, translate code? (Open to ideas!)
• Configuration: Centralized config file (e.g., ~/.config/codex-cli-studio/config.toml).
• PyPI Release: Package and publish for easy pip install.
• IDE Integration: VS Code plugin?

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🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to check issues page.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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📄 License

Distributed under the MIT License. See LICENSE file for more information.

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→ Apply this to real-world workflows. Use AI like magic — right from your terminal.