npx skills add ...
npx skills add nvidia/skills --skill i4h-workflow-e2e
Run the maintained workflow data-to-policy pipeline from recording through checkpoint validation. Use for full end-to-end requests; do not use for one individual stage.
npx skills add nvidia/skills --skill i4h-workflow-e2e
Use the maintained driver so stage resolution, artifacts, logs, and checkpoint handoff stay consistent with current workflow/task manifests.
Treat this resolver as part of the skill contract: a hosted copy may run outside the base repository, so never assume the current checkout contains workflows/i4h_workflows. I4H_WORKFLOWS_REPO_URL selects the clone source. When I4H_WORKFLOWS is unset, derive the fallback directory from that URL; set I4H_WORKFLOWS only to reuse or choose a specific destination. Never replace an existing checkout.
Require the workflow's policy mode. The driver discovers the remote task, embodiment, task text, and trainability from live workflow/task manifests.
Require exit status 0 and inspect every printed command and artifact path. The dry-run is the source of truth for current stages and backend ownership.
Use --run-dir only when the caller needs a specific location. Apply --skip-mimic, --skip-annotate, --skip-replay, or --skip-viz only when the user explicitly omits that optional stage or a documented smoke profile requires it.
Keep the driver as this agent's foreground tool call. Do not use a subagent, monitor task, shell backgrounding, nohup, tmux, or a detached process. Poll until exit.
The driver performs full setup, then owns its stage sequence, timestamped run directory, runs/.latest link, and per-stage logs. Do not replace it with a manually assembled subset.
On success, inspect the printed summary and artifacts:
On failure, stop at the first failed stage, inspect that stage's log, preserve the run directory, and repair the owning stage before rerunning. Do not skip a required failure merely to obtain a green summary. Stop leftovers with ./stop.sh all.
Use the first failed stage and its log to choose the owning stage skill. Preserve the run directory and rerun only after that stage verifies its output.
Require a policy workflow plus host, simulator, backend, VLM, dataset, training, and visualization dependencies for every enabled stage.
The pipeline supports only workflows with a policy mode; inference-only Tasks skip fine-tuning and checkpoint validation.
Run end-to-end smoke pipeline for scissor pick-and-place. → dry-run, execute the driver, and report each recording-to-validation stage.Report workflow/task/embodiment/trainability, dry-run result, run directory, every stage outcome and skip, dataset/visualizer/checkpoint/verification artifacts, final exit status, and cleanup state.