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Task: {{ task }} Identify the dataset this task needs and download/stage it into ./data under the workspace. If ./data already contains the dataset, do nothing and finish.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | download_data |
| description | Task: {{ task }} Identify the dataset this task needs and download/stage it into ./data under the workspace. If ./data already contains the dataset, do nothing and finish. |
| tools | ["read_file","write_file","run_command","append_file"] |
SKILL_ID: download_data
You stage the dataset for an ML task. The workspace venv (torch, torchvision,
numpy, scikit-learn) is auto-activated for every command — just call python ....
Steps:
download_data.py that downloads BOTH the train and test splits into
./data using torchvision (download=True, root="data"), then prints the number
of train/test examples.python download_data.py. If it errors, fix and rerun.Run a tiny human-in-the-loop session: ask the user a couple of questions on the console, then write a short personalized note from their answers.
Task: {{ task }} Implement (or, if a proposal is given below, modify) the ML pipeline so it trains and evaluates end-to-end. Target metric to beat: {{ target_accuracy }}. Proposed change for THIS experiment (empty on the baseline): {{ current_proposal | default("(none — build a simple baseline)") }}
Task: {{ task }} Best VALIDATION accuracy (the hill-climb selection metric): {{ best_score }} (target {{ target_accuracy }}, higher is better). Held-out TEST accuracy of the retrained winner — the HEADLINE number, selected on validation and reported once on the test set: {{ final_test_score }}. Write the final HTML research report for this ML auto-research run.
Review the applied change against the proposal, check the contract, and run the smoke tests. Decide pass or fail. Proposal that was supposed to be applied: {{ current_proposal | default("(none — baseline build)") }}
Competition: {{ competition_id }} Metric: {{ metric_name }} ({{ "lower is better" if lower_is_better else "higher is better" }}). Final best validation score: {{ best_score }} (target {{ target_score }}). Write the final HTML report for this kaggle-solver run.
Condense the current kaggle experiment proposal into one short paragraph for the running research log.