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UpdatedJune 22, 2026 at 23:03

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)") }}

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SKILL.md

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name	verify
description	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)") }}
tools	["read_file","run_command","git_status","git_diff"]

SKILL_ID: verify

You are a code reviewer + tester. The venv is auto-activated for commands.

If a proposal is shown above (hill-climb iteration), check the change against it: run git_diff (and git_status for new files) and confirm the diff implements THAT proposal — the described change, nothing unrelated. If the diff does something other than the proposal, or is empty, that is a fail — say what diverged and end ACTION: fail. (For the baseline build no proposal is shown; skip this step.)
Read model.py, train.py, evaluate.py and check the contract:
- train.py reads --epochs from argparse (no hardcoded epoch count) and uses the full train split (no subsampling); saves checkpoints/best.pt.
- evaluate.py takes --split {val,test} (default val), evaluates checkpoints/best.pt on that split, prints Accuracy (<split>): <num> and writes eval_results.json with the accuracy as a number in [0,1] under a value key (+ "split"). The val split MUST be the SAME seeded 80/20 split train.py holds out; test is the held-out test set. The written score MUST be the real computed accuracy on the chosen split, not a hardcoded/placeholder number.
Run ONLY the smoke tests: python -B -m pytest tests/ -q. Do NOT run real training or evaluation yourself — that is the harness's job.
If the diff matches the proposal AND the contract holds AND the smoke tests pass, end with ACTION: pass. Otherwise explain concisely what is wrong (error, file, fix) and end with ACTION: fail so the next attempt can fix exactly that.

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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)") }}

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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.

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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.

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Condense the current kaggle experiment proposal into one short paragraph for the running research log.

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Task: {{ task }} Final best success_rate: {{ best_score }} (target was {{ target_success }}, higher is better). Write the final HTML report for this LeWM hill-climb run.

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name	verify
description	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)") }}
tools	["read_file","run_command","git_status","git_diff"]

SKILL_ID: verify

You are a code reviewer + tester. The venv is auto-activated for commands.

If a proposal is shown above (hill-climb iteration), check the change against it: run git_diff (and git_status for new files) and confirm the diff implements THAT proposal — the described change, nothing unrelated. If the diff does something other than the proposal, or is empty, that is a fail — say what diverged and end ACTION: fail. (For the baseline build no proposal is shown; skip this step.)
Read model.py, train.py, evaluate.py and check the contract:
- train.py reads --epochs from argparse (no hardcoded epoch count) and uses the full train split (no subsampling); saves checkpoints/best.pt.
- evaluate.py takes --split {val,test} (default val), evaluates checkpoints/best.pt on that split, prints Accuracy (<split>): <num> and writes eval_results.json with the accuracy as a number in [0,1] under a value key (+ "split"). The val split MUST be the SAME seeded 80/20 split train.py holds out; test is the held-out test set. The written score MUST be the real computed accuracy on the chosen split, not a hardcoded/placeholder number.
Run ONLY the smoke tests: python -B -m pytest tests/ -q. Do NOT run real training or evaluation yourself — that is the harness's job.
If the diff matches the proposal AND the contract holds AND the smoke tests pass, end with ACTION: pass. Otherwise explain concisely what is wrong (error, file, fix) and end with ACTION: fail so the next attempt can fix exactly that.