Kaggle

This skill implements powerful automation for Kaggle — including account setup, pushing/running kernels, submitting to competitions, creating datasets/models, and an automated badge-farming system. Before installing or running it: - Verify which credential the scripts actually read/write (KAGGLE_API_TOKEN vs KAGGLE_KEY vs KAGGLE_USERNAME) and where (it will write to ~/.kaggle/access_token and/or ~/.kaggle/kaggle.json and may offer to write a .env). - Inspect the registration and badge-collector scripts (e.g., modules/registration/* and modules/badge-collector/*) yourself. They perform network calls, create resources, and submit files on your behalf. - Do not run the badge-farming or submission phases from your primary/personal Kaggle account — use an isolated test account, because automated submissions, mass resource creation, or streak automation may violate Kaggle Terms of Service. - The skill lists Python and pip dependencies (kagglehub, kaggle, requests, python-dotenv, optional playwright) but provides no install spec; ensure you manually install and audit those packages and Playwright before running. - Prefer running with --dry-run and --status first to see planned actions and inspect badge-progress.json; review any cron/launchd setup steps before enabling persistent tasks. - If you decide to proceed, create and use limited/test credentials and revoke them after verification. If anything looks unexpected (posting to endpoints other than api.kaggle.com / www.kaggle.com / storage.googleapis.com, exfiltration of unrelated files, or attempts to access other system credentials), stop and do not run the scripts. Given the mismatches and the potential for abusive automation, proceed only after manual code review and using an isolated account.