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nvidia/skills98 installs

i4h-workflow

Orient users to the i4h workflow runtime and route them to the correct stage skill. Use for architecture, support, or where-to-start questions; do not execute a known stage.

How do I install this agent skill?

npx skills add https://github.com/nvidia/skills --skill i4h-workflow
view source ↗

Is this agent skill safe to install?

  • Gen Agent Trust Hubpass

    The skill manages orientation and routing for the Isaac for Healthcare (i4h) workflow. It downloads the project's repository and executes scripts for workflow discovery and cleanup. While these are standard developer tools, the process involves executing code from an external repository and ingesting data that could be used for indirect prompt injection.

  • Socketpass

    No alerts

  • Snykpass

    Risk: LOW · No issues

What does this agent skill do?

i4h Workflows

Purpose

Orient the user from live repository facts, then hand execution to the narrowest stage skill.

Instructions

  1. Run the base-checkout resolver.
  2. Read live support and DESIGN.md.
  3. Use only current architecture facts in the answer.
  4. Use the narrowest stage skill for execution.

Resolve the checkout

export I4H_WORKFLOWS_REPO_URL="${I4H_WORKFLOWS_REPO_URL:-https://github.com/isaac-for-healthcare/i4h-workflows}"
I4H_REPO_DIR_NAME="${I4H_WORKFLOWS_REPO_URL%/}"
I4H_REPO_DIR_NAME="${I4H_REPO_DIR_NAME##*/}"
I4H_REPO_DIR_NAME="${I4H_REPO_DIR_NAME##*:}"
I4H_REPO_DIR_NAME="${I4H_REPO_DIR_NAME%.git}"
[ -n "$I4H_REPO_DIR_NAME" ] || { echo "Cannot derive a checkout name from I4H_WORKFLOWS_REPO_URL" >&2; exit 2; }
ROOT="${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>/dev/null)}"
if [ ! -d "$ROOT/workflows/i4h_workflows" ]; then
  ROOT="${I4H_WORKFLOWS:-$HOME/$I4H_REPO_DIR_NAME}"
  [ -d "$ROOT/workflows/i4h_workflows" ] || git clone "$I4H_WORKFLOWS_REPO_URL" "$ROOT"
fi
export I4H_WORKFLOWS="$ROOT"
cd "$ROOT"

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.

Inspect before answering

Read ./DESIGN.md for architecture and skills/i4h-workflow/references/repo-map.md for ownership. Discover current support instead of copying a static table:

./run.sh list

If discovery fails because setup is incomplete, report that limitation and route to i4h-workflow-setup.

Explain the design

Keep the summary precise:

  • A Scene owns the simulated world, assets, embodiment, cameras, randomization, adapters, and reset hooks.
  • A Task owns one reusable capability. It reads ctx.scene, writes ctx.act, and never advances the simulator.
  • A Workflow selects one Scene, exposes run-mode-specific TaskGraph builders, and owns goal semantics. A run mode answers how that workflow should run; code and CLI use the shorter term mode.
  • The Engine schedules graph nodes; the shared SimulationRunner alone resets, steps, renders, records, retries whole episodes, and prints run summaries.
  • Online RL is a separate training lifecycle: its trainer owns vectorized stepping and returns a checkpoint to the normal policy Task and SimulationRunner validation path.
  • Simulator-compatible exported RSL-RL actors may run as in-process Tasks; incompatible foundation-model policy stacks remain remote.
  • Remote policy stacks run out of process and communicate over Zenoh; offline dataset tools remain independent of the simulator.
  • Python owns behavior. Manifests carry facts across dependency boundaries.

Do not describe retired environment YAMLs, per-mode runners, or separate policy/Arena launchers.

Route the next action

GoalSkill
Install, sync, or repair dependenciesi4h-workflow-setup
Create a new workflow/environmenti4h-workflow-create
Edit an existing scene, camera, task, or success rulei4h-workflow-scene-edit
Record demonstrationsi4h-workflow-dataset-teleop
Replay HDF5i4h-workflow-dataset-replay
Augment HDF5i4h-workflow-dataset-mimic
Grade/filter HDF5 with a VLMi4h-workflow-dataset-annotate
Convert HDF5 to LeRoboti4h-workflow-dataset-convert
Inspect LeRobot in a browseri4h-lerobot-viz
Fine-tune a manifest-backed policy taski4h-workflow-finetune
RL post-train a supported policy in simulationi4h-workflow-train-rl
Run policy or rule-based rolloutsi4h-workflow-validate
Run the maintained complete pipelinei4h-workflow-e2e

For Stop all, do not load a stage skill. Run ./stop.sh all from the repository root and report the stopped process count.

Troubleshooting

If discovery fails, verify the resolved checkout and run setup. If a mode is absent, report it as unsupported.

Prerequisites

Require a readable base checkout or network access to clone it.

Limitations

This router does not install, author, simulate, process data, train, or evaluate.

Examples

  • What does the i4h workflow include, and where should I start? → inspect live support, summarize DESIGN.md, and recommend one stage skill.

Completion gate

Answer with the live workflow/mode list, a short architecture summary, and one concrete next skill. If the requested workflow or mode is absent from run.sh list, say it is unsupported instead of inventing a command.

Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.

<a href="https://skillzs.dev/skills/nvidia/skills/i4h-workflow">View i4h-workflow on skillZs</a>