数据

agentic-kaggle-skill

Guides an AI agent through end-to-end Kaggle competitions, including validation, modeling, OOF predictions, ensembling, remote notebook pipelines, artifact handoffs, submission scoring, and debugging.

★ 185 GitHub Stars
88 质量评分
1 文件

关于此 Skill

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.

使用场景

  • Building Kaggle competition baselines and submissions
  • Designing leakage-safe cross-validation and OOF workflows
  • Running multi-notebook producer and consumer pipelines
  • Offloading heavy training to Kaggle notebooks
  • Debugging failed code-competition scoring runs

功能

  • Kaggle-first competition operating loop
  • Task-specific validation and modeling guidance
  • OOF-based blending and stacking workflow
  • Producer and consumer notebook architecture
  • Submission scoring and hidden-test debugging loop

要求

  • Python
  • Kaggle CLI
  • Kaggle credentials

支持的 Agent

Codex

Skill 文件

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