Add getting-started marimo notebook

First marimo-based UV script demonstrating dual-mode execution:
- Interactive tutorial via `uvx marimo edit --sandbox`
- Batch script via `uv run`

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

README.md

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+---
+viewer: false
+tags:
+  - uv-script
+  - marimo
+  - tutorial
+---
+
+# Marimo UV Scripts
+
+[Marimo](https://marimo.io/) notebooks that work as both **interactive tutorials** and **batch scripts**.
+
+## What is this?
+
+Marimo notebooks are pure Python files that can be:
+- **Edited interactively** with a reactive notebook interface
+- **Run as scripts** with `uv run` - same as any UV script
+
+This makes them perfect for tutorials and educational content where you want users to explore step-by-step, but also run the whole thing as a batch job.
+
+## Available Scripts
+
+| Script | Description |
+|--------|-------------|
+| `getting-started.py` | Introduction to UV scripts and HF datasets |
+
+## Usage
+
+### Run as a script
+
+```bash
+# Run directly from Hugging Face
+uv run https://huggingface.co/datasets/uv-scripts/marimo/raw/main/getting-started.py --help
+
+# Load a dataset and show info
+uv run https://huggingface.co/datasets/uv-scripts/marimo/raw/main/getting-started.py --dataset squad
+```
+
+### Run interactively
+
+```bash
+# Clone and edit locally
+git clone https://huggingface.co/datasets/uv-scripts/marimo
+cd marimo
+
+# Open in marimo editor (--sandbox auto-installs dependencies)
+uvx marimo edit --sandbox getting-started.py
+```
+
+### Run on HF Jobs
+
+```bash
+hf jobs uv run --flavor cpu-basic \
+    -e HF_TOKEN=$(python3 -c "from huggingface_hub import get_token; print(get_token())") \
+    https://huggingface.co/datasets/uv-scripts/marimo/raw/main/getting-started.py \
+    --dataset squad
+```
+
+## Why Marimo?
+
+- **Reactive**: Cells automatically re-run when dependencies change
+- **Pure Python**: No JSON, git-friendly, readable as plain code
+- **Self-contained**: Inline dependencies via PEP 723 metadata
+- **Dual-mode**: Same file works as notebook and script
+
+## Learn More
+
+- [Marimo documentation](https://docs.marimo.io/)
+- [UV scripts guide](https://docs.astral.sh/uv/guides/scripts/)
+- [uv-scripts organization](https://huggingface.co/uv-scripts)

getting-started.py

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+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#     "marimo",
+#     "datasets",
+#     "huggingface-hub",
+# ]
+# ///
+"""
+Getting Started with UV Scripts + Marimo
+
+This notebook demonstrates how marimo notebooks can work as both:
+- Interactive tutorials: `uvx marimo edit --sandbox getting-started.py`
+- Batch scripts: `uv run getting-started.py --dataset squad`
+
+Run with --help to see available options.
+"""
+
+import marimo
+
+app = marimo.App(width="medium")
+
+
+@app.cell
+def _():
+    import marimo as mo
+
+    mo.md(
+        """
+        # Getting Started with UV Scripts
+
+        This notebook shows how to work with Hugging Face datasets using UV scripts.
+
+        **Two ways to run this:**
+        - **Interactive**: `uvx marimo edit --sandbox getting-started.py`
+        - **As a script**: `uv run getting-started.py --dataset squad`
+        """
+    )
+    return (mo,)
+
+
+@app.cell
+def _(mo):
+    import argparse
+    import sys
+
+    parser = argparse.ArgumentParser(
+        description="Load and explore a Hugging Face dataset"
+    )
+    parser.add_argument(
+        "--dataset",
+        default="stanfordnlp/imdb",
+        help="Dataset to load (default: stanfordnlp/imdb)",
+    )
+    parser.add_argument(
+        "--split",
+        default="train",
+        help="Split to load (default: train)",
+    )
+    parser.add_argument(
+        "--num-samples",
+        type=int,
+        default=5,
+        help="Number of samples to display (default: 5)",
+    )
+
+    # Parse known args to avoid issues with marimo's internal args
+    args, _ = parser.parse_known_args()
+
+    mo.md(
+        f"""
+        ## Configuration
+
+        - **Dataset**: `{args.dataset}`
+        - **Split**: `{args.split}`
+        - **Samples to show**: `{args.num_samples}`
+        """
+    )
+    return args, argparse, parser, sys
+
+
+@app.cell
+def _(args, mo):
+    from datasets import load_dataset
+
+    mo.md(f"Loading dataset `{args.dataset}`...")
+    return (load_dataset,)
+
+
+@app.cell
+def _(args, load_dataset, mo):
+    # Load the dataset
+    dataset = load_dataset(args.dataset, split=args.split)
+
+    mo.md(
+        f"""
+        ## Dataset Info
+
+        - **Name**: {args.dataset}
+        - **Split**: {args.split}
+        - **Number of rows**: {len(dataset):,}
+        - **Features**: {list(dataset.features.keys())}
+        """
+    )
+    return (dataset,)
+
+
+@app.cell
+def _(args, dataset, mo):
+    # Show sample data
+    samples = dataset.select(range(min(args.num_samples, len(dataset))))
+
+    mo.md(f"## Sample Data (first {len(samples)} rows)")
+    return (samples,)
+
+
+@app.cell
+def _(samples):
+    # Display as a table (marimo will render this nicely)
+    samples.to_pandas()
+    return
+
+
+@app.cell
+def _(mo):
+    mo.md(
+        """
+        ## Next Steps
+
+        Now that you've seen the basics, try:
+
+        1. **Change the dataset**: Run with `--dataset emotion` or any HF dataset
+        2. **Explore interactively**: Edit cells, add visualizations
+        3. **Run as batch**: `uv run getting-started.py --dataset squad --num-samples 10`
+
+        Check out more UV scripts at [uv-scripts on Hugging Face](https://huggingface.co/uv-scripts)
+        """
+    )
+    return
+
+
+if __name__ == "__main__":
+    app.run()