boot-code 0.1.0

Spec-driven, build passing code.

👋 Welcome to Boot!

Your AI-powered code generator. Write a 5-line spec. Get a working code base that passes tests and builds.

Boot uses AI to generate production-ready PySpark, Python, and SQL pipelines from simple specifications. No more boilerplate. No more setup hassle. Just describe what you want.

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Install (30 seconds)

Step 1: Prerequisites

First, ensure you have the required tools installed on your system:

Python: You'll need Python 3.11 or newer. You can check your version with python --version.

Poetry: This project uses Poetry for dependency management. If you don't have it, install it using the officially recommended method:

This command downloads and runs the official installer

pip install poetry

After installation, close and reopen your terminal, or run source "$HOME/.poetry/env" to ensure the poetry command is available in your PATH. You can find more details at the official Poetry documentation.

Step 2: Project Setup

Once the prerequisites are met, you can set up the project:

First, ensure you have an API key from OpenAI and/or Google Gemini.

Next, set up your environment:

# 1. Clone the repository
git clone [e-mail]:renbytes/boot-code.git
cd boot-code

# 2. Install project dependencies
poetry install

# 3. Start a virtual environment
poetry env activate

# 3. Set up your API keys
cp .env.example .env

# 4. Edit the .env file to add your keys
# OPENAI_API_KEY="sk-..."
# GEMINI_API_KEY="AI..."

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Use (2 minutes)

Run the main CLI module and follow the prompts:

# Activate the environment
poetry shell

# View available commands
boot --help

# Example: Validate a specification file
boot validate path/to/your/spec.toml

# Example: Generate a new pipeline
boot generate path/to/your/spec.toml

That's it. Answer a few questions, and watch your pipeline appear.

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What You Get

• Complete project structure with all the files you need.
• Working code that's ready to run on your data.
• Unit tests to ensure quality and reliability.
• Visualizations to help you see your results.
• Documentation so your team understands the pipeline.

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Why Boot?

Instead of spending hours writing boilerplate, Boot generates:

✅ Production-ready code following best practices
✅ Complete test suites with 90%+ coverage
✅ Interactive visualizations for immediate insights
✅ Professional documentation your team will love
✅ Modern tooling (Streamlit, Black, pytest)

Performance Comparison

Metric	Manual Coding	With Boot
Time to MVP	4-8 hours	2 minutes
Lines of code	200-500	Generated
Test coverage	~60%	90%+
Documentation	Minimal	Complete

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Advanced Usage

Below is an example of more advanced usage, using all available flags:

boot generate examples/consumer_tech/spec.toml \
--provider gemini \ 
--model gemini-2.5-pro \
--api-key "AIzaSy..." \
--two-pass \
--temperature 0.1 \
--timeout 180 \
--output-dir Desktop/ecommerce_gemini_pro

poetry run boot generate examples/rust/my_rust_spec.toml --build-pass

Where:

• --provider: Explicitly selects the LLM provider, overriding any default or .env setting.
• --model: Specifies a particular model to use for the generation, rather than the default.
• --api-key: Provides the API key directly on the command line, which takes precedence over any key in an .env file or other environment settings.
• --two-pass: Enables the secondary review pass, where the initial code is sent back to the LLM for refinement and improvement
• --temperature: Sets the generation temperature to a very low value, making the output more deterministic and less random.
• --timeout: Sets the API request timeout, which is useful for complex specifications that may take the model longer to process.
• --output-dir: Specifies a custom directory for the generated project files, overriding the default generated_jobs location.

Examples

Explore real-world use cases in the examples/ directory:

• E-commerce - Top selling products analysis (SQL)
• Healthcare - Patient length of stay analysis (SQL)
• Finance - Stock volatility calculation (Python)
• Energy - Renewable energy production analysis (Python)
• Consumer Tech - Ad attribution pipeline (PySpark)

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Supported Languages

Language	Framework	Use Case
Python	pandas	Data analysis, reporting
PySpark	Spark	Big data, distributed computing
SQL	dbt-style	Data warehousing, analytics

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Development

See the DEVELOPER_GUIDE.md

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Support

• 📖 Documentation: Check the docs/ directory.
• 🐛 Issues: Report bugs on GitHub Issues.