القائمة
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.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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 | 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.