About

Imported from nvidia/skills (skills/i4h-lerobot-viz/SKILL.md). Install upstream with npx skills add nvidia/skills --skill i4h-lerobot-viz. Copyright stays with the author (Apache-2.0).

Visualize a LeRobot Dataset

Name: i4h-lerobot-viz
Author: nvidia

Purpose

Serve one completed local dataset and verify its episode videos and timelines in a browser.

Instructions

Resolve the base checkout and one completed LeRobot dataset.
Launch its managed local server.
Open the printed URL.
Inspect videos, timelines, episode count, and cleanup state.

Resolve the dataset

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 "$ROOT"
find runs "${HF_LEROBOT_HOME:-$HOME/.cache/huggingface/lerobot}" \
  -name info.json -path '*/meta/*' -printf '%T@ %h\n' 2>/dev/null \
  | sort -nr | head

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/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.

Use the explicit/current-chain converted directory. Otherwise select the newest candidate and state the choice. Require <dataset>/meta/info.json; route raw HDF5 to i4h-workflow-dataset-convert.

Serve

Pass an absolute local path:

DATASET_DIR=/absolute/path/to/lerobot/dataset
STATE_DIR=/absolute/path/to/run/viz-state
tools/dataset/scripts/viz.sh "$DATASET_DIR" --state-dir "$STATE_DIR"

The script selects a free local port, waits for HTTP readiness, and prints the URL, PID, state files, log, and exact stop command. Keep it running only while the user wants access.

Verify visually

Open the printed URL. Confirm:

the requested episode list loads
every expected camera video renders
state and action timelines render with plausible dimensions and motion
episode count and task text match meta/info.json
no blank page, missing video, or wrong dataset is being served

Reuse a live server only when its target dataset matches. Otherwise stop it using its printed state directory and port, then start the requested dataset.

Troubleshooting

If startup or the page fails, inspect meta/info.json, videos, the printed server log, and port ownership before restarting.

Prerequisites

Require the dataset tool environment and a completed LeRobot dataset with meta/info.json.

Limitations

The visualizer does not accept raw workflow HDF5 or repair incomplete metadata/videos.

Examples

Open the latest converted LeRobot dataset for inspection. → choose the newest verified dataset, serve it, and report the observed videos/timelines plus cleanup command.

Completion gate

Report dataset path, repo id, local URL, episode/camera/timeline observations, PID/state directory, and exact cleanup command.

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

yaml

targets:
  - https://api.opensmartroute.ai/api/v1/registry/nvidia-skills-i4h-lerobot-viz/manifest   # or paste the manifest below

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

nvidia-skills-i4h-lerobot-viz.ocm.jsonjson

{
  "ocm": "1",
  "id": "nvidia-skills-i4h-lerobot-viz",
  "kind": "skill",
  "name": "i4h-lerobot-viz",
  "description": "Serve and visually inspect a converted LeRobot dataset in the browser. Use for videos and state/action timelines; do not use for raw workflow HDF5 or incomplete conversion output.",
  "publisher": "nvidia",
  "version": "1.0.0",
  "capabilities": {
    "domains": [
      "data_analysis"
    ],
    "tags": [
      "skill-md",
      "isaac-for-healthcare",
      "i4h",
      "dataset",
      "lerobot",
      "visualization",
      "skills-sh"
    ],
    "languages": [
      "en"
    ]
  },
  "quality_prior": 0.6,
  "examples": [
    "Serve and visually inspect a converted LeRobot dataset in the browser. Use for videos and state/action timelines; do not use for raw workflow HDF5 or incomplete conversion output."
  ],
  "primary": false,
  "metadata": {
    "source": {
      "provider": "skills.sh",
      "repository": "https://github.com/nvidia/skills",
      "path": "skills/i4h-lerobot-viz/SKILL.md",
      "ref": "HEAD",
      "url": "https://github.com/nvidia/skills/blob/HEAD/skills/i4h-lerobot-viz/SKILL.md",
      "key": "nvidia/skills/skills/i4h-lerobot-viz/SKILL.md"
    },
    "license": "Apache-2.0"
  },
  "instructions": "# Visualize a LeRobot Dataset\n\n## Purpose\n\nServe one completed local dataset and verify its episode videos and timelines in a browser.\n\n## Instructions\n\n1. Resolve the base checkout and one completed LeRobot dataset.\n2. Launch its managed local server.\n3. Open the printed URL.\n4. Inspect videos, timelines, episode count, and cleanup state.\n\n## Resolve the dataset\n\n```bash\nexport I4H_WORKFLOWS_REPO_URL=\"${I4H_WORKFLOWS_REPO_URL:-https://github.com/isaac-for-healthcare/i4h-workflows}\"\nI4H_REPO_DIR_NAME=\"${I4H_WORKFLOWS_REPO_URL%/}\"\nI4H_REPO_DIR_NAME=\"${I4H_REPO_DIR_NAME##*/}\"\nI4H_REPO_DIR_NAME=\"",
  "cost": {
    "context_tokens": 854
  }
}

Fetch it by URL: GET /api/v1/registry/nvidia-skills-i4h-lerobot-viz/manifest?version=1.0.0

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