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I4h Lerobot Viz

NVIDIAOfficialCreated by Isaac for Healthcare TeamDistributed by NVIDIA

  • Skill
  • Tool integration
  • Data and AI
  • Apache-2.0
  • Medium risk
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From the author

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.
— the description by Isaac for Healthcare Team

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a mechanism to serve and visually validate converted LeRobot datasets. It enables users to inspect episode videos, state and action timelines, and metadata to ensure the dataset is correctly formatted and complete. It is designed for post-conversion verification and requires a completed dataset with a valid meta/info.json file.

What it does

  • Locates and identifies converted LeRobot datasets.
  • Launches a local web server to host the dataset for inspection.
  • Provides a browser-based interface to view camera videos and data timelines.
  • Verifies episode counts and task information against metadata.
  • Manages server lifecycle, including startup and cleanup commands.

When to use it

  • After converting a dataset to the LeRobot format.
  • When you need to visually verify that episode videos and timelines are rendering correctly.
  • To confirm that metadata matches the expected dataset content.
  • Before proceeding with downstream tasks that require a verified dataset.

Advantages

  • Enables direct visual confirmation of dataset integrity.
  • Provides clear feedback on state and action timeline accuracy.
  • Simplifies the verification process through a browser-based interface.
  • Includes automated cleanup instructions to manage local server resources.

Usage examples

“Open the latest converted LeRobot dataset for inspection.”
The agent identifies the most recent dataset, launches the local visualization server, provides the access URL, and summarizes the observed videos and timelines.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/i4h-lerobot-viz/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip i4h-lerobot-viz.zip -d .agents/skills

Documentation by Isaac for Healthcare Team

Visualize a LeRobot Dataset

Purpose

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

Instructions

  1. Resolve the base checkout and one completed LeRobot dataset.

  2. Launch its managed local server.

  3. Open the printed URL.

  4. 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.

Files

skill-card.md

Description: <br>

Serve and visually inspect a converted LeRobot dataset in the browser. <br>

This skill is ready for commercial/non-commercial use. <br>

Owner

NVIDIA <br>

License/Terms of Use: <br>

Apache-2.0 <br>

Use Case: <br>

Developers and engineers use this skill to serve and visually inspect converted LeRobot datasets in a browser, verifying episode videos, state/action timelines, and metadata integrity. <br>

Deployment Geography for Use: <br>

Global <br>

Requirements / Dependencies: <br>

Requires API Key or External Credential: [Not Specified] <br> Credential Type(s): [None identified] <br>

Do not include secrets in prompts/logs/output; use least-privilege credentials; rotate keys as appropriate. <br>

Known Risks and Mitigations: <br>

Risk: Review before execution as proposals could introduce incorrect or misleading guidance into skills. <br> Mitigation: Review and scan skill before deployment. <br>

Reference(s): <br>

Skill Output: <br>

Output Type(s): [Shell commands, Analysis] <br> Output Format: [Markdown with inline bash code blocks] <br> Output Parameters: [1D] <br> Other Properties Related to Output: [None] <br>

Evaluation Agents Used: <br>

  • Claude Code (aws/anthropic/bedrock-claude-opus-4-8) <br>

  • Codex (openai/openai/gpt-5.5) <br>

Evaluation Tasks: <br>

Evaluated against 2 tasks (2 positive) in isolated k8s-sandbox pods with 3 attempts per task. <br>

Evaluation Metrics Used: <br>

Reported benchmark dimensions: <br>

  • Security: Is it safe to use? Checks for unsafe operations, secret leakage, and unauthorized access. <br>

  • Correctness: Is the answer correct? Measures final-answer correctness against the reference answer. <br>

  • Discoverability: Was the right skill loaded when needed? Checks whether the expected skill was selected and the workflow executed. <br>

  • Effectiveness: Did the skill help complete the task? Equal-weight mean of goal completion and expected workflow adherence. <br>

  • Efficiency: Did it avoid wasted tool calls and token usage? Combines tool-call productivity and token efficiency. <br>

Underlying evaluation signals used in this run: <br>

  • security: Unsafe operations, secret leakage, and unauthorized access. <br>

  • skill_execution: Whether the expected skill was selected, decoys were avoided, and the workflow executed. <br>

  • skill_efficiency: Tool-call productivity (routing scored under Discoverability). <br>

  • accuracy: Final-answer correctness against the reference answer. <br>

  • goal_accuracy: Whether the user's goal was achieved. <br>

  • behavior_check: Whether the expected workflow behavior was followed. <br>

  • token_efficiency: Actual uncached prompt plus completion usage. <br>

Evaluation Results: <br>

MeasureClaude Code (Baseline → Skill Uplift)Codex (Baseline → Skill Uplift)
Overall81.7% — baseline ran, but no comparable score was available; uplift unavailable74.2% — baseline ran, but no comparable score was available; uplift unavailable
Security100.0% → 100.0% (±0.0 points)100.0% → 66.7% (-33.3 points)
Correctness35.0% → 100.0% (+65.0 points)45.0% → 100.0% (+55.0 points)
Discoverability95.0% — baseline ran, but no comparable score was available; uplift unavailable93.3% — baseline ran, but no comparable score was available; uplift unavailable
Effectiveness6.3% → 16.3% (+10.0 points)2.5% → 23.3% (+20.8 points)
Efficiency97.3% — baseline ran, but no comparable score was available; uplift unavailable87.8% — baseline ran, but no comparable score was available; uplift unavailable

Skill Version(s): <br>

0.8.0 (source: frontmatter) <br>

Ethical Considerations: <br>

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal team to ensure this skill meets requirements for the relevant industry and use case and addresses unforeseen product misuse. <br>

(For Release on NVIDIA Platforms Only) <br> Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here. <br>

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
Isaac for Healthcare Team
Publisher
NVIDIA (Official)
Distributed by
NVIDIA
Source
NVIDIA/skills · skills/i4h-lerobot-viz · commit fce3cca
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE-APACHE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Isaac for Healthcare Team or NVIDIA.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download i4h-lerobot-viz.zip