Exécutez n'importe quel Skill dans Manus
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Exécutez n'importe quel Skill dans Manus en un clic

implement

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Mis à jour20 juin 2026 à 17:36

Task: {{ task }} Apply EXACTLY this proposed change to the le-wm repository (the workspace): {{ current_proposal | default("(none)") }}

Installation

Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.

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Source

cgpadwick

cgpadwick/saage

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Métiers associésSOC

Basé sur la classification professionnelle SOC

Développeurs de logicielsProfessions informatiques et mathématiques·SOC 15-1252

SKILL.md

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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:

Change ONLY what the proposal asks, in the file(s) it names — typically 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).
NEVER touch eval.py, anything under config/eval/, trainer.max_epochs, output_model_name, subdir, or the wandb/trackio logging setup.
DO NOT run training or evaluation (not even a short one) — the verify step smoke-tests your change and the harness does the real run.

Sanity-check your edit cheaply before finishing:

YAML edits: python -c "import yaml; yaml.safe_load(open('config/train/lewm.yaml'))" (same for any other YAML you touched).
Code edits: 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.

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interview

cgpadwick/saage

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.

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implement

cgpadwick/saage

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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report

cgpadwick/saage

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.

2026-06-227

verify

cgpadwick/saage

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)") }}

2026-06-227

report

cgpadwick/saage

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.

2026-06-227

summarize

cgpadwick/saage

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

2026-06-227

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:

Change ONLY what the proposal asks, in the file(s) it names — typically 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).
NEVER touch eval.py, anything under config/eval/, trainer.max_epochs, output_model_name, subdir, or the wandb/trackio logging setup.
DO NOT run training or evaluation (not even a short one) — the verify step smoke-tests your change and the harness does the real run.

Sanity-check your edit cheaply before finishing:

YAML edits: python -c "import yaml; yaml.safe_load(open('config/train/lewm.yaml'))" (same for any other YAML you touched).
Code edits: 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.