Competition Workflows

Competition Workflows

Overview

Use this skill for Kaggle competition workflows from data access through notebook execution and submission. Prioritize reproducibility, rule compliance, and leakage prevention over leaderboard-chasing shortcuts.

Submission from Notebook Output

Use the notebook-kernel submission form when submitting an output file produced by a notebook version:

kaggle competitions submit <competition> -k <owner/slug> -f <file> -v <version> -m "<message>"

Before submission, confirm the notebook run completed successfully, the output file exists, and the version number matches the intended run. Use notebook-lifecycle for status, logs, files, and output download commands.

Competition Data Access

Use Kaggle CLI or kagglehub for competition downloads. Attach competition sources in kernel-metadata.json when the notebook must run on Kaggle. Confirm users accepted competition rules before assuming downloads or submissions will work.

Submission Validation Checklist

• Assert required columns, order, row count, and ID coverage.
• Check for nulls, infinities, invalid labels, and out-of-range predictions.
• Save outputs under /kaggle/working during Kaggle runs.
• Verify the submitted filename exactly matches the generated artifact.
• Include a concise submission message that identifies model/run changes without exposing secrets.

Leakage and Validation Review

Use group/time/stratified splits that match the competition structure. Apply preprocessing inside folds to avoid fitting transforms on validation data. Avoid using public leaderboard feedback as a validation set; repeated leaderboard probing can overfit. Confirm external data, pretrained models, internet access, and ensemble sources comply with rules.

Reproducibility Standards

Notebook should execute top-to-bottom from a clean Kaggle session. Set seeds for Python, NumPy, framework libraries, and splitters where applicable. Pin package versions when environment drift could affect results. Use a DEBUG flag to run small samples locally or during fast checks without changing final-run logic.

Common Failure Modes

Safety and Limits

Do not bypass competition rules or suggest hidden test reconstruction, private data scraping, or external data not allowed by rules. Warn before long GPU/TPU training runs. If secrets are needed, advise Kaggle Secrets through the UI; do not claim public API support for secrets administration.

Sources

• Kaggle API docs: https://www.kaggle.com/docs/api
• Kaggle notebooks docs: https://www.kaggle.com/docs/notebooks
• Kaggle kernels CLI: https://github.com/Kaggle/kaggle-api/blob/main/docs/kernels.md