Kaggle Master

Kaggle `kernel-metadata.json` setup and repair. PROACTIVELY activate for: (1) creating kernel-metadata.json, (2) fixing metadata validation errors, (3) setting `id`, `title`, `code_file`, language, or kernel type, (4) configuring private/public, internet, GPU, or accelerator behavior, (5) attaching dataset_sources, competition_sources, kernel_sources, or model_sources, (6) preparing metadata before `kaggle kernels push`, (7) converting script/notebook metadata. Provides: schema checklist, valid fields, source arrays, defaults, and push-readiness review.

Kaggle runtime environment, paths, accelerators, and reproducibility. PROACTIVELY activate for: (1) `/kaggle/input` path errors, (2) `/kaggle/working` output placement, (3) local vs Kaggle notebook behavior, (4) GPU/TPU/accelerator selection, (5) internet enablement, (6) package/version pinning, (7) memory cleanup and timeout issues, (8) DEBUG flags for fast runs, (9) Kaggle Secrets usage guidance, (10) quota-consuming runtime settings. Provides: path conventions, runtime checklist, accelerator IDs, reproducibility patterns, and resource safeguards.

Kaggle notebook/kernel lifecycle operations. PROACTIVELY activate for: (1) creating Kaggle notebooks or kernels, (2) `kaggle kernels init`, (3) pushing or running notebooks with `kaggle kernels push`, (4) pulling notebooks with metadata, (5) checking kernel status, files, logs, or outputs, (6) downloading output artifacts with file patterns, (7) deleting kernels, (8) Python API kernel operations, (9) auth for notebook CLI workflows. Provides: safe CLI/Python lifecycle commands, run monitoring, output retrieval, destructive-action guardrails.

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.

Kaggle datasets, models, sources, and kagglehub workflows. PROACTIVELY activate for: (1) downloading datasets with kagglehub, (2) uploading datasets, (3) downloading or uploading Kaggle models, (4) competition_download, (5) notebook_output_download, (6) choosing Kaggle CLI vs kagglehub, (7) attaching dataset_sources, competition_sources, kernel_sources, or model_sources, (8) model artifact transfer, (9) source dependency cleanup, (10) kagglehub limitations for notebooks. Provides: dataset/model transfer patterns, source attachment guidance, tool selection, and limitation checks.