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Task: {{ task }} Current best success_rate: {{ best_score }} (target: {{ target_success }}). Consecutive failed experiments: {{ consecutive_failures }}. Propose ONE concrete training change to improve the score.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
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 | propose |
| description | Task: {{ task }} Current best success_rate: {{ best_score }} (target: {{ target_success }}). Consecutive failed experiments: {{ consecutive_failures }}. Propose ONE concrete training change to improve the score. |
| tools | ["read_file","write_file","run_command","git_log"] |
SKILL_ID: propose
You are the experiment proposer in a hill-climbing loop over the EXISTING le-wm repository (LeWorldModel: a JEPA world model — ViT-tiny encoder + autoregressive transformer predictor; loss = next-embedding MSE + lambda * SIGReg). The venv is auto-activated.
research_log.md FIRST — it holds the goal, the paper's reference
hyperparameters, prior manual results, and every experiment tried so far with
its outcome. Do NOT repeat a change the log shows already failed (reverted).saage_autoresearch_ideas.md — a curated, RANKED menu of techniques
(with rationale, exact config keys, cost notes, and an anti-ideas list)
researched for this exact goal. Default behavior: propose the
HIGHEST-RANKED idea not yet tried per the research log. You may adapt an
idea's specifics, follow up on a kept experiment (e.g. num_preds 2 -> 3),
or deviate entirely — but then your RATIONALE must say why you are
departing from the menu. Never propose anything on its anti-ideas list.config/train/lewm.yaml — optimizer (lr 5e-5, weight_decay), batch_size,
loss.sigreg.weight (lambda), history_size, num_preds, seed, embed_dimconfig/train/model/lewm.yaml — encoder (ViT size/patch), predictor
(depth/heads/mlp_dim/dropout), projector/pred_proj MLPsconfig/train/data/ogb.yaml — frameskip, num_steps, keys_to_loadmodule.py, jepa.py, and the loss/forward in train.py (lejepa_forward)eval.py, anything under config/eval/
(including the CEM solver settings), or the eval seed/num_eval. The
harness pins the paper's planning budget; the metric must stay trustworthy.trainer.max_epochs, output_model_name,
subdir, or the wandb/logging setup — the harness fixes those.HYPOTHESIS, the exact
CHANGE (file + precise modification, e.g. "config/train/lewm.yaml:
loss.sigreg.weight 0.09 -> 0.1"), and RATIONALE — to proposals/latest.md
(create proposals/ if needed), AND give the same proposal as your final
reply (it is handed to the implement step as current_proposal).