Official agent skill

I4h Workflow Dataset Convert

by NVIDIA in NVIDIA/skills

Convert workflow HDF5 recordings to LeRobot datasets for training or browser inspection.

OfficialApache-2.0Auto-check passed

Install I4h Workflow Dataset Convert

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

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

GitHub CLI
$ gh skill install NVIDIA/skills i4h-workflow-dataset-convert --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-convert .claude/skills/i4h-workflow-dataset-convert && 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-convert
GitHub stars
3.5k
Used in
1 other repo
Token cost
~1.3k tokens
SKILL.md length
460 words
Files
5
Skills in repo
380
Repo updated
First seen
Licence
Apache-2.0

At a glance

Convert workflow HDF5 recordings to LeRobot datasets for training or browser inspection.

  • Works in 4 steps: Run the checkout resolver and select the… → Read the workflow, Scene, embodiment,… → Run conversion for the selected… → …
  • Do not use for replay
  • SKILL.md covers Purpose, Instructions, Resolve and inspect and Convert, plus 6 more sections
  • Calls git and uv; reaches github.com

What it does

I4h Workflow Dataset Convert is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Convert workflow HDF5 recordings to LeRobot datasets for training or browser inspection. Use for conversion; do not use for replay, augmentation, or raw-data repair.

Its SKILL.md is about 1.3k 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 replay
  • Raw-data repair

Example prompts

  • “/i4h-workflow-dataset-convert”

Workflow steps

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

  1. Run the checkout resolver and select the source HDF5.
  2. Read the workflow, Scene, embodiment, and instruction.
  3. Run conversion for the selected successful episodes.
  4. Inspect metadata, parquet, videos, and feature widths.

What it can do on your machine

Read from SKILL.md and the folder at commit 67a13c0. 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 Convert loads about 1.3k tokens when it runs. Until then it costs about 49 tokens; SKILL.md has 460 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~49
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k

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 67a13c0, republished under its Apache-2.0 licence (© NVIDIA). 460 words, ~1,330 tokens.

Download SKILL.mdSave it as .claude/skills/i4h-workflow-dataset-convert/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-convert
description
Convert workflow HDF5 recordings to LeRobot datasets for training or browser inspection. Use for conversion; do not use for replay, augmentation, or raw-data repair.
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, hdf5, lerobot

Convert Workflow HDF5 to LeRobot

Purpose

Preserve recorded actions, state, cameras, task text, and embodiment labels in a local LeRobot dataset.

Instructions

  1. Run the checkout resolver and select the source HDF5.
  2. Read the workflow, Scene, embodiment, and instruction.
  3. Run conversion for the selected successful episodes.
  4. Inspect metadata, parquet, videos, and feature widths.

Resolve and inspect

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 "$ROOT"
HDF5_PATH=/absolute/path/to/recording.hdf5
uv run --project tools/dataset i4h-dataset inspect "$HDF5_PATH" --segments

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 recording. Resolve its workflow and Scene from recording metadata/context, then read the Scene manifest for the embodiment and instruction. Use the embodiment manifest for labels. Do not assume state width equals action width; the converter derives both from the recording.

Convert

bash
RUN_DIR="$(pwd)/runs/<workflow>/$(date +%Y%m%d_%H%M%S)"
DATASET_DIR="$RUN_DIR/lerobot/local/<name>"
mkdir -p "$(dirname "$DATASET_DIR")"
[ ! -e "$DATASET_DIR" ] || { echo "Destination already exists: $DATASET_DIR" >&2; exit 2; }
uv run --project tools/dataset i4h-dataset convert \
  "$HDF5_PATH" "$DATASET_DIR" \
  --robot <embodiment> \
  --repo-id "local/<name>" \
  --successful-only \
  --task "<instruction>"

Use --fps or --skip-frames only when the user requests it or source metadata justifies it. Keep the default H.264 video codec for compatibility with GR00T's fast decord loader; select another --video-codec only when the target consumer requires it.

Conversion writes aggregate meta/stats.json for downstream policy loaders. Native G1 rule-based WBC recordings already contain 43-D state and 50-D action; the converter recognizes that contract and writes GR00T's required semantic meta/modality.json automatically. For a G1 recording made through the legacy 23-D Pink/keyboard contract and destined for a 50-D G1 WBC policy Task, add --g1-wbc-policy-actions. That explicit mapping combines the measured 43-joint state with the recorded navigation, base-height, and torso commands; require source action width 23 and state width 43.

Show full SKILL.md (182 more words)Show less

Verify

Require:

  • meta/info.json
  • meta/stats.json
  • meta/modality.json when the target trainer requires semantic modality slices
  • episode parquet data
  • video files for every recorded camera
  • converted episode count matching selected successful sources
  • action/state feature widths and names matching the recording plus embodiment descriptor

For G1, require modality metadata for both supported paths: native state=43/action=50, or explicitly mapped state=43/source-action=23/output-action=50. Treat a native 50-D dataset without meta/modality.json as incomplete.

Treat missing inputs or zero converted episodes as failure. If conversion leaves a partial destination, quarantine or remove that exact incomplete directory before retrying; never report it as usable.

Troubleshooting

On dimension errors, resolve the source workflow and embodiment again. On missing videos, confirm frames existed before conversion.

Prerequisites

Require a readable HDF5 recording and its matching Scene plus embodiment manifests.

Limitations

Conversion cannot reconstruct missing cameras, actions, state, task text, or successful episodes.

Examples

  • Convert my scissor pick-and-place recording into a LeRobot dataset. → resolve so101, convert successful episodes, and verify metadata, parquet, and both camera videos.

Completion gate

Report source HDF5/workflow, embodiment, task text, source/converted/skipped counts, action/state widths, output directory/repo id, aggregate-stats/modality/parquet/video checks, and any missing modality.

© 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-convert of NVIDIA/skills.

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

Open the folder on GitHubat commit 67a13c0

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 Convert 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 Convert compared with similar skills
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Convertremotion-dev/remotion62k—~247Automated safety check: PassCustom licence
Convert DatasetAgibotTech/genie_sim1.4k—~1.5kAutomated safety check: NotesMPL-2.0
Recordingcodewhale-hq/Codewhale41k—~540Automated safety check: PassMIT
Train Poseruvnet/RuView97k—~504Automated safety check: PassMIT

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Questions about I4h Workflow Dataset Convert

What does I4h Workflow Dataset Convert do?

Convert workflow HDF5 recordings to LeRobot datasets for training or browser inspection. I4h Workflow Dataset Convert is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Convert workflow HDF5 recordings to LeRobot datasets for training or browser inspection.

When should I use I4h Workflow Dataset Convert?

I4h Workflow Dataset Convert fits situations like: do not use for replay; raw-data repair.

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

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

How do I install I4h Workflow Dataset Convert in Codex?

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

Can I use I4h Workflow Dataset Convert 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-convert -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-convert, .gemini/skills/i4h-workflow-dataset-convert, .github/skills/i4h-workflow-dataset-convert and .opencode/skills/i4h-workflow-dataset-convert in your project.

What does I4h Workflow Dataset Convert need to run?

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

Does I4h Workflow Dataset Convert 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 Convert 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 Convert use?

I4h Workflow Dataset Convert 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 Convert use?

About 1.3k tokens (SKILL.md is roughly 5.3k 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 Convert?

Skills that share tags, products or a category with I4h Workflow Dataset Convert: Converting Recorders To Services (quarkusio/quarkus, 16k stars), Convert (remotion-dev/remotion, 62k stars), Convert Dataset (AgibotTech/genie_sim, 1.4k stars) and Recording (codewhale-hq/Codewhale, 41k 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 Convert?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,539 GitHub stars. The repository holds 380 skills in this directory. The repository was last updated on October 7, 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.