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Run GapONet data preparation. Use when the user invokes /gaponet-data or $gaponet-data, or asks to process, validate, stage, copy, symlink, or prepare GapONet motion .npz data for training or inference.
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
| name | gaponet-data |
| description | Run GapONet data preparation. Use when the user invokes /gaponet-data or $gaponet-data, or asks to process, validate, stage, copy, symlink, or prepare GapONet motion .npz data for training or inference. |
Run the GapONet data stage, not just a review. Work from the GapONet repository root, identified by scripts/prepare_gaponet_data.py, scripts/run_gaponet_job.py, and configs/.
.npz, stage it into the default operator motion path:python scripts/prepare_gaponet_data.py --input-data <input.npz> --output-dir ./runs/data --mode copy --profile operator --force --update-configs
python scripts/prepare_gaponet_data.py --config configs/data_default.json
.npz path. Do not invent or synthesize motion data.runs/data/data_manifest.json status, the staged output_data, and any validation errors.source/sim2real/sim2real/tasks/humanoid_operator/motions/motion_amass/edited_27dof/test.npzconfigs/data_default.jsonruns/data/data_manifest.json--update-configs: configs/deploy_default.json and input_package_example.jsonUse the operator profile by default. It requires:
real_dof_positionsreal_dof_velocitiesreal_dof_positions_cmdreal_dof_torquesjoint_sequencepayloadsUse --profile deploy only when the user explicitly wants lightweight inference validation that does not require the operator training keys.
Consider the data stage complete only when data_manifest.json has "status": "success" and the staged .npz path exists. If Python lacks NumPy, tell the user to activate the GapONet environment before rerunning the data stage.
GapONet sim-to-real humanoid robot control workflows for Isaac Lab. Use when working on the GapONet repository or tasks involving GapONet setup, environment validation, training, evaluation, checkpoint export, lightweight deployment, DeepONet, Transformer or MLP policy configs, humanoid operator or AMASS tasks, motion data schemas, robot asset integration, or agent-friendly run artifacts.
Run GapONet inference and evaluation. Use when the user invokes /gaponet-inf or $gaponet-inf, or asks to export a GapONet checkpoint to TorchScript/JIT, run lightweight inference, deploy a policy, evaluate test motion data, or produce inference metrics.
Run GapONet training. Use when the user invokes /gaponet-train or $gaponet-train, or asks to train a GapONet humanoid policy after data preparation using Isaac Lab, Isaac Sim, rsl_rl, DeepONet, Transformer, MLP, or the unified runner.