Official agent skill

I4h Workflow Dataset Teleop

by NVIDIA in NVIDIA/skills

Record demonstrations through a workflow's teleop Task into workflow HDF5.

OfficialApache-2.0Auto-check passed

Install I4h Workflow Dataset Teleop

skills CLI
$ npx skills add NVIDIA/skills --skill i4h-workflow-dataset-teleop -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install NVIDIA/skills i4h-workflow-dataset-teleop --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/i4h-workflow-dataset-teleop .claude/skills/i4h-workflow-dataset-teleop && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
i4h-workflow-dataset-teleop
GitHub stars
3.5k
Used in
1 other repo
Token cost
~1.2k tokens
SKILL.md length
425 words
Files
5
Skills in repo
386
Repo updated
First seen
Licence
Apache-2.0

At a glance

Record demonstrations through a workflow's teleop Task into workflow HDF5.

  • Works in 4 steps: Resolve the base checkout and live… → Choose a supported human-input device. → Record in the foreground through run.sh. → …
  • Do not use for policy evaluation
  • SKILL.md covers Purpose, Instructions, Resolve support and Choose input, plus 7 more sections
  • Calls git and uv; reaches github.com

What it does

I4h Workflow Dataset Teleop is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Record demonstrations through a workflow's teleop Task into workflow HDF5. Use for keyboard, leader, VR, or bus input; do not use for policy evaluation or autonomous rule-based Tasks.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files (for example `BENCHMARK.md`, `evals/evals.json` and `skill-card.md`).

The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.

When your agent uses it

  • Do not use for policy evaluation
  • Autonomous rule-based Tasks

Example prompts

  • “/i4h-workflow-dataset-teleop”

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Resolve the base checkout and live teleop device contract.
  2. Choose a supported human-input device.
  3. Record in the foreground through run.sh.
  4. Inspect visible motion and HDF5 content.

What it can do on your machine

Read from SKILL.md and the folder at commit dfdd080. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git
    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

I4h Workflow Dataset Teleop loads about 1.2k tokens when it runs. Until then it costs about 53 tokens; SKILL.md has 425 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~53
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from NVIDIA/skills at commit dfdd080, republished under its Apache-2.0 licence (© NVIDIA). 425 words, ~1,184 tokens.

Download SKILL.mdSave it as .claude/skills/i4h-workflow-dataset-teleop/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
i4h-workflow-dataset-teleop
description
Record demonstrations through a workflow's teleop Task into workflow HDF5. Use for keyboard, leader, VR, or bus input; do not use for policy evaluation or autonomous rule-based Tasks.
license
Apache-2.0
metadata.author
Isaac for Healthcare Team <isaac-for-healthcare-support@nvidia.com>
metadata.version
0.8.0
metadata.verification-request
2026-09-15
metadata.tags
isaac-for-healthcare, i4h, dataset, teleoperation, hdf5

Record Teleop Demonstrations

Purpose

Run the workflow's declared teleop graph through the shared SimulationRunner so actions, state, cameras, segments, attempts, and outcomes use the normal HDF5 contract.

Instructions

  1. Resolve the base checkout and live teleop device contract.
  2. Choose a supported human-input device.
  3. Record in the foreground through run.sh.
  4. Inspect visible motion and HDF5 content.

Resolve support

bash
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"
./run.sh list
./run.sh show <workflow> --mode teleop

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.

Require teleop in the live mode list. Read the workflow builder, its scene manifest teleop override, and the embodiment manifest's teleop_devices. Do not maintain a static support table in the skill.

Choose input

  • Use the named device when supported.
  • Otherwise use the workflow builder's default device.
  • Keep interactive keyboard/leader/VR/bus sessions visible and in the foreground. Surface the device controls and require the human operator to complete the task.
  • Never pretend to provide human input in an unattended shell.

Record

bash
./run.sh <workflow> --teleop <device> \
  --episodes <N> --attempts 3 \
  --record

Omit <device> to use the workflow default. Bare --record writes demos.hdf5 inside the launcher's automatic run directory. Read the absolute directory from the ==> run dir ... line or run.json; do not recreate its timestamp in the shell. When a larger pipeline requires a caller-selected shared directory, pass --run-dir "$RUN_DIR" --record demos.hdf5; the launcher creates the directory and anchors the relative recording name inside it. An absolute --record path remains supported.

Show full SKILL.md (159 more words)Show less

Verify

Require the final N/N episodes succeeded summary. Then inspect content:

bash
RUN_DIR="<absolute run_dir from run.json or launcher output>"
uv run --project tools/dataset i4h-dataset inspect "$RUN_DIR/demos.hdf5" --segments
uv run --project tools/dataset i4h-dataset actions "$RUN_DIR/demos.hdf5"

Visually confirm that the operator completes the requested task, robot motion matches the input device, and all expected cameras record the same behavior. Treat zero saved episodes, missing observations, absent action motion, or an unsuccessful task outcome as failure. Stop leftovers with ./stop.sh all.

Troubleshooting

On device or width errors, compare the workflow teleop builder, Scene mode override, and embodiment devices.

Prerequisites

Require a workflow with teleop, a supported device, a working simulator, and a present operator for interactive input.

Limitations

Teleop records human input and requires an operator for interactive devices. Record autonomous rule-based Tasks with i4h-workflow-validate instead.

Examples

  • Record 5 keyboard teleop demonstrations for locomanip tray pick and place. → use G1's supported keyboard device, require a human operator, and verify the recorded action motion.

Completion gate

Report workflow, mode/device, controls, requested/saved episodes, attempts, visual result, HDF5 path, dimensions/segments, and whether a human operator completed the task.

© NVIDIA, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files in skills/i4h-workflow-dataset-teleop of NVIDIA/skills.

  • SKILL.md
  • BENCHMARK.md
  • evals/evals.json
  • skill-card.md
  • skill.oms.sig

Open the folder on GitHubat commit dfdd080

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in NVIDIA/skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

I4h Workflow Dataset Teleop next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

I4h Workflow Dataset Teleop compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
I4h Workflow Dataset Teleop this skillNVIDIA/skills3.5k1 repos~1.2kAutomated safety check: PassApache-2.0
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DatasetsArize-ai/phoenix12k—~1.6kAutomated safety check: PassCustom licence
Architecture Decision Recordsaffaan-m/ECC276k4 repos~1.8kAutomated safety check: PassMIT
Architecture Decision Recordsaffaan-m/ECC276k1 repos~863Automated safety check: PassMIT
Architecture Decision Recordsaffaan-m/ECC276k—~1.1kAutomated safety check: PassMIT

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Questions about I4h Workflow Dataset Teleop

What does I4h Workflow Dataset Teleop do?

Record demonstrations through a workflow's teleop Task into workflow HDF5. I4h Workflow Dataset Teleop is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Record demonstrations through a workflow's teleop Task into workflow HDF5.

When should I use I4h Workflow Dataset Teleop?

I4h Workflow Dataset Teleop fits situations like: do not use for policy evaluation; autonomous rule-based Tasks.

How do I install I4h Workflow Dataset Teleop in Claude Code?

Run `npx skills add NVIDIA/skills --skill i4h-workflow-dataset-teleop -a claude-code`. Or copy the skill folder (skills/i4h-workflow-dataset-teleop in NVIDIA/skills) into .claude/skills/i4h-workflow-dataset-teleop in your project. Claude Code loads it when a task matches its description.

How do I install I4h Workflow Dataset Teleop in Codex?

Run `npx skills add NVIDIA/skills --skill i4h-workflow-dataset-teleop -a codex`. Or copy the skill folder (skills/i4h-workflow-dataset-teleop in NVIDIA/skills) into .agents/skills/i4h-workflow-dataset-teleop in your project. Codex loads it when a task matches its description.

Can I use I4h Workflow Dataset Teleop in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill i4h-workflow-dataset-teleop -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/i4h-workflow-dataset-teleop, .gemini/skills/i4h-workflow-dataset-teleop, .github/skills/i4h-workflow-dataset-teleop and .opencode/skills/i4h-workflow-dataset-teleop in your project.

What does I4h Workflow Dataset Teleop need to run?

Going by SKILL.md and its folder, I4h Workflow Dataset Teleop needs the command-line tools its instructions call (git and uv).

Does I4h Workflow Dataset Teleop access the network?

SKILL.md names 1 domain. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is I4h Workflow Dataset Teleop safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does I4h Workflow Dataset Teleop use?

I4h Workflow Dataset Teleop is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does I4h Workflow Dataset Teleop use?

About 1.2k tokens (SKILL.md is roughly 4.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to I4h Workflow Dataset Teleop?

Skills that share tags, products or a category with I4h Workflow Dataset Teleop: Recording (codewhale-hq/Codewhale, 41k stars), Datasets (Arize-ai/phoenix, 12k stars), Architecture Decision Records (affaan-m/ECC, 276k stars) and Architecture Decision Records (affaan-m/ECC, 276k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains I4h Workflow Dataset Teleop?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,546 GitHub stars. The repository holds 386 skills in this directory. The repository was last updated on October 9, 2026.

Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.