competition-workflows
Kaggle competition notebook workflows and submissions. PROACTIVELY activate for: (1) submitting notebook outputs to competitions, (2) `kaggle competitions submit -k`, (3) downloading competition data, (4) validating submission.csv format, (5) leakage review, (6) cross-validation split design, (7) public leaderboard overfitting concerns, (8) competition rule compliance, (9) reproducible top-to-bottom notebook execution, (10) fold-aware preprocessing for ML pipelines. Provides: submission commands, validation checklist, leakage controls, and competition-ready notebook guidance.
What this skill does
# 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: ```bash 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 | Symptom | Check | |---|---| | Submission rejected | Filename, columns, rows, competition slug, accepted rules | | Score impossible | Leakage, target contamination, ID mismatch, train/test merge mistake | | Notebook output missing | Save location, run failure, timeout, file pattern | | Local score diverges | Split mismatch, fold leakage, random seeds, preprocessing outside folds | ## 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
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