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Kaggle-first end-to-end competition workflow for scored submissions. Use when Codex must run Kaggle or competitive ML workflows through scored submission, including code competitions, validation, metrics, policy-safe public notebook/discussion intel, tabular/text/image modeling, tuning, ensembling/stacking, proactive multi-notebook architectures, producer notebooks that train models and export private Kaggle artifact datasets, downstream consumer notebooks, Kaggle GPU offload, kagglehub access, hidden-test debugging, and public score retrieval.
agentic-kaggle-skill is an agent skill entry in ChatHome Skills, sourced from GitHub Skill Search and scored 58/100. # Agentic Kaggle Skill ## Operating Loop Treat every competition as a validation problem first and a modeling problem second. The default target platform is Kag. As an agent skill, it packages a reusable instruction set that AI coding agents such as Codex and Claude Code can load to perform this task without hand-written glue code. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding agentic-kaggle-skill to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
codex skill install FrankS-IntelLab/agentic-kaggle-skillReview the upstream source before running this command.
Scan coverage: Install metadata and the indexed document body.
Automated signals from install metadata and a bounded skill body — not a full code security audit and not a recursive source or dependency review.
# Agentic Kaggle Skill ## Operating Loop Treat every competition as a validation problem first and a modeling problem second. The default target platform is Kaggle, so prefer Kaggl