Agent skill · NVIDIA
i4h-workflow-finetune
Fine-tune a manifest-backed GR00T or openpi remote Task on compatible LeRobot data. Use for training; do not use for inference-only Tasks or checkpoint rollout.
What it needs
About 3k tokens when loaded.
What this skill does
Fine-tune a Workflow Policy Task Purpose Resolve and run training from the selected workflow run mode and owning remote-task manifest. Instructions 1. Resolve the base checkout, policy mode, and remote task. 2. Verify dataset compatibility and a train block. 3. Dry-run the exact configuration. 4. Train in the foreground and verify checkpoint artifacts. Resolve the workflow, task, and data Treat the resolver above as part of the skill contract: a hosted copy may run outside the base repository, so never assume the current checkout contains workflows/i4hworkflows. I4HWORKFLOWSREPOURL selects the clone source. When I4HWORKFLOWS is unset, derive the fallback directory from that URL; set I4HWORKFLOWS only to reuse or choose a specific destination. Never replace an existing checkout. Read the selected workflow run mode to identify its remote task id. Open tasks/<project>/i4htasks/<project>/manifest/<task>.yaml and require train:. Resolve the project, entry point, base model/config, output defaults, and modality contract from that manifest and the project's train.py. Use the current-chain LeRobot dataset when the prompt omits a path. Verify its embodiment, cameras, task text, feature widths, and episode count are compatible with the remote task. Resolve configuration before a long run All policy train entry points support --dry-run: Inspect the resolved config. Keep user-requested steps, batch size, model/config, and GPU count exact. For GR00T, “turn off vision tuning” maps to --no-tune-visual. Do not pass that flag to openpi, whose CLI does not expose it. Use only flags present in the selected project's current train.py. Train Remove --dry-run and keep the command in the foreground: Add backend-specific flags only after resolving them. Do not silently lower requested steps or batch size to make training fit. Verify Require exit status 0, completed requested steps, saved training logs, and at least one loadable checkpoint artifact. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills NVIDIA/i4h-workflow-finetune