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Perform exploratory data analysis and write data_analysis.md. Competition context: competition_understanding.md (read it first).
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
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.
| name | eda |
| description | Perform exploratory data analysis and write data_analysis.md. Competition context: competition_understanding.md (read it first). |
| tools | ["read_file","write_file","run_command"] |
SKILL_ID: eda
You are performing EDA for a Kaggle competition. This is EDA ONLY — do NOT train models or write solution code.
WORKFLOW:
competition_understanding.md for the problem and data modalities.run_command (python eda_overview.py etc.). The venv has pandas, numpy,
matplotlib, sklearn. Use MPLBACKEND=Agg — never open GUI windows; save
plots to files if you make any.data_analysis.md with the findings.data_analysis.md MUST include:
End your reply with a one-paragraph summary of the most important findings.