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Sanity-check the training run that just finished (captured validation score: {{ candidate_score }}). End with ACTION: pass or ACTION: fail.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
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 | verify_training |
| description | Sanity-check the training run that just finished (captured validation score: {{ candidate_score }}). End with ACTION: pass or ACTION: fail. |
| tools | ["read_file","run_command"] |
SKILL_ID: verify_training
A deterministic training command just ran (train.py). Decide whether it
actually trained — not whether the score is good.
run_command: tail -50 training.log if it
exists, else check ls checkpoints/ and cat eval_results.json.When failing, summarize the actual error from the log in 1-3 lines (this is re-injected as feedback for the retry).
End your reply with ACTION: pass or ACTION: fail.