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Task: {{ task }} Apply EXACTLY this proposed change to the le-wm repository (the workspace): {{ current_proposal | default("(none)") }}
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
| name | implement |
| description | Task: {{ task }} Apply EXACTLY this proposed change to the le-wm repository (the workspace): {{ current_proposal | default("(none)") }} |
| tools | ["read_file","write_file","edit_file","append_file","run_command","git_status","git_diff"] |
SKILL_ID: implement
You are an ML engineer working in the EXISTING le-wm repository. The venv (torch, lightning, stable-pretraining, stable-worldmodel, hydra) is auto-activated for commands.
You APPLY THE PROPOSAL ONLY. A separate, deterministic harness runs training ({{ train_epochs }} epochs, fixed) and evaluation — so every experiment is comparable. Therefore:
config/train/lewm.yaml, config/train/model/lewm.yaml,
config/train/data/ogb.yaml, module.py, jepa.py, or the loss in
train.py. Keep everything else identical (git_diff shows your footprint).eval.py, anything under config/eval/, trainer.max_epochs,
output_model_name, subdir, or the wandb/trackio logging setup.Sanity-check your edit cheaply before finishing:
python -c "import yaml; yaml.safe_load(open('config/train/lewm.yaml'))"
(same for any other YAML you touched).python -c "import jepa, module" must import cleanly.When the change is applied and the sanity checks pass, finish with a one-line summary of exactly what you changed. If feedback from a previous attempt is shown in the task, fix exactly that.
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.