Metadata-Version: 2.4
Name: signalpilot
Version: 0.2.2
Summary: SignalPilot CLI - Your Trusted CoPilot for Data Analysis
Project-URL: Homepage, https://signalpilot.ai
Project-URL: Repository, https://github.com/SignalPilot-Labs/sp-cli
Project-URL: Documentation, https://signalpilot.ai/docs
Author-email: "SignalPilot Inc." <info@signalpilot.ai>
License: MIT
License-File: LICENSE
Keywords: ai,copilot,data-analysis,jupyter,notebook
Classifier: Development Status :: 3 - Alpha
Classifier: Framework :: Jupyter
Classifier: Framework :: Jupyter :: JupyterLab
Classifier: Framework :: Jupyter :: JupyterLab :: 4
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development
Requires-Python: >=3.10
Requires-Dist: rich>=13.0.0
Requires-Dist: typer>=0.12.0
Provides-Extra: dev
Requires-Dist: pytest>=8.0.0; extra == 'dev'
Description-Content-Type: text/markdown

# SignalPilot CLI

Your Trusted CoPilot for Data Analysis - A simple CLI tool to bootstrap Jupyter-powered data science workspaces with AI agent support.

## Features

- 🚀 **One-command setup** - Get from zero to Jupyter Lab in under 3 minutes
- ⚡  **Python 3.12** - Uses the latest Python with uv for fast package management
- 📊 **Pre-configured workspace** - Includes pandas, numpy, matplotlib, seaborn, plotly
- 🤖 **AI-ready** - Built-in SignalPilot AI agent support
- ⚡ **Fast** - Optimized Jupyter cache for quick startups
- �� **Beautiful CLI** - Clean, colorful terminal output

## Quick Start

```bash
# Install uv (if you don't have it)
brew install uv  # macOS
# OR
curl -LsSf https://astral.sh/uv/install.sh | sh  # Linux/macOS

# Run SignalPilot init (no installation needed!)
uvx signalpilot init
```

That's it! The tool will:
1. Create `~/SignalPilotHome` directory structure
2. Download starter notebook and dependencies
3. Set up Python 3.12 virtual environment
4. Install Jupyter Lab and data science packages
5. Optimize Jupyter cache for fast startup

## Usage

After running `sp init`, follow the next steps:

```bash
cd ~/SignalPilotHome && source .venv/bin/activate
jupyter lab
```

## What Gets Installed

**Python Packages:**
- `signalpilot-ai` - AI agent integration
- `jupyterlab` - Modern Jupyter interface
- `pandas`, `numpy` - Data manipulation
- `matplotlib`, `seaborn`, `plotly` - Visualization
- `python-dotenv`, `tomli` - Configuration utilities

**Directory Structure:**
```
~/SignalPilotHome/
├── user-skills/       # Custom agent skills
├── user-rules/        # Custom agent rules
├── team-workspace/    # Shared team notebooks
├── demo-project/      # Example notebooks
├── start-here.ipynb   # Quick start guide
└── .venv/             # Python environment
```

## Requirements

- Python 3.10 or higher
- [uv](https://docs.astral.sh/uv/) package manager

## Permanent Installation Options (Not Recommended)

### Option 1: Consider Running with uvx (Recommended - no installation needed)
```bash
uvx signalpilot init
```

### Option 2: Install with uv
```bash
uv tool install signalpilot
sp init
```

### Option 3: Install with pip
```bash
pip install signalpilot
sp init
```

## License

MIT License - See LICENSE file for details

## Links

- [Homepage](https://signalpilot.ai)
- [Documentation](https://docs.signalpilot.ai)
- [GitHub](https://github.com/SignalPilot-Labs/sp-cli)