kagglelisted

<objective> Generate a Kaggle competition notebook script in Jupytext `# %%` format. Follows jirka's ML research style distilled from past notebooks: - **PTL always for DNN training** (PyTorch Lightning + torchmetrics) — even simple baselines - **Tool agnostic** — pick best-fit library for the problem; use PTL when training loop needed - **Stages with lenses** — each major stage includes a quick sanity check cell (show one batch, print shapes, verify submission format) - **`!` bash over subprocess** — package installs, `nvidia-smi`, `ls -lh`, `! head submission.csv` - **EDA is visual** — distribution plots, sample grids, dimension scatters before any model - **Inference included** — model save pattern + separate load-and-infer cells - **CSVLogger + seaborn** — metrics plotted from `metrics.csv` after every training run NOT for writing Python packages, modules, or production code — notebook scripts only. NOT a research literature survey — use `/research:topic` for SOTA literature search. </objective> <inputs> - **$ARGUMENTS**: one of: - `<competition-name>` — short slug used for output filename; generates blank template - `<competition-name> <url>` — fetches competition overview from URL before generating - `<competition-name> "<description>"` — uses inline description of problem and data - `--type <type>` — hint: `classification`, `regression`, `segmentation`, `detection`, `tabular` (auto-detected when omitted) - `--eda-only` — generate only EDA sections (no m