Official agent skill

I4h Workflow Dataset Replay

by NVIDIA in NVIDIA/skills

Replay a workflow HDF5 episode through its original Scene. An agent skill from NVIDIA/skills.

OfficialApache-2.0Auto-check passed

Install I4h Workflow Dataset Replay

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

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

GitHub CLI
$ gh skill install NVIDIA/skills i4h-workflow-dataset-replay --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-replay .claude/skills/i4h-workflow-dataset-replay && 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-replay
GitHub stars
3.6k
Used in
1 other repo
Token cost
~972 tokens
SKILL.md length
333 words
Files
5
Skills in repo
390
Repo updated
First seen
Licence
Apache-2.0

At a glance

Replay a workflow HDF5 episode through its original Scene. An agent skill from NVIDIA/skills.

  • Works in 4 steps: Run the checkout resolver and select the… → Read the original workflow and requested… → Inspect action-contract compatibility. → …
  • Visual trajectory and recording verification
  • SKILL.md covers Purpose, Instructions, Resolve the recording and Inspect before launch, plus 7 more sections
  • Calls git and uv; reaches github.com

What it does

I4h Workflow Dataset Replay is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Replay a workflow HDF5 episode through its original Scene. Use for visual trajectory and recording verification; do not use for policy evaluation or LeRobot data.

Its SKILL.md is about 970 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

  • Visual trajectory and recording verification
  • Do not use for policy evaluation

Example prompts

  • “/i4h-workflow-dataset-replay”

Workflow steps

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

  1. Run the checkout resolver and select the exact HDF5.
  2. Read the original workflow and requested episode.
  3. Inspect action-contract compatibility.
  4. Run the replay visibly in the foreground through completion.

What it can do on your machine

Read from SKILL.md and the folder at commit 14a98ae. 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 Replay loads about 972 tokens when it runs. Until then it costs about 48 tokens; SKILL.md has 333 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~48
When it runs · the whole SKILL.md, loaded when a task matches
~972

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 14a98ae, republished under its Apache-2.0 licence (© NVIDIA). 333 words, ~972 tokens.

Download SKILL.mdSave it as .claude/skills/i4h-workflow-dataset-replay/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-replay
description
Replay a workflow HDF5 episode through its original Scene. Use for visual trajectory and recording verification; do not use for policy evaluation or LeRobot data.
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, replay, hdf5

Replay a Workflow Recording

Purpose

Replay the exact recorded action sequence through the matching workflow and inspect it visibly.

Instructions

  1. Run the checkout resolver and select the exact HDF5.
  2. Read the original workflow and requested episode.
  3. Inspect action-contract compatibility.
  4. Run the replay visibly in the foreground through completion.

Resolve the recording

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"
find runs -name '*.hdf5' -type f -printf '%T@ %p\n' | 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 an explicit or current-chain recording first. Otherwise select the newest plausible HDF5 and state that choice. Never substitute an older file after a failed recording.

Interpret natural ordinals as zero-based indices: first is 0, second is 1.

Inspect before launch

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

Resolve the original workflow from recording metadata and conversation context. Confirm the requested episode exists and its action width matches the workflow's replay Scene contract.

Replay

bash
./run.sh <workflow> --replay "$HDF5_PATH" --episode <zero-based-index>

Keep the visible simulator and command in the foreground. Poll yielded execution until run.sh exits; do not detach or return while replay is still running.

Verify

Observe the complete trajectory, relevant objects, camera views, segment boundaries, and final status. If motion diverges, report the first mismatching segment or action-contract error. Do not change workflow, episode, or recording silently.

Troubleshooting

On launch failure, verify workflow metadata, episode existence, and action width. On divergence, report the first mismatching segment.

Prerequisites

Require the original workflow assets and a readable episode whose action width matches replay mode.

Limitations

Replay verifies stored actions in one Scene; it does not evaluate a learned policy or guarantee transfer to another Scene.

Examples

  • Replay the second episode. → select the current recording, map “second” to index 1, and observe the visible replay.

Completion gate

Report workflow, HDF5 path, episode index, frame/segment count, action width, exit/final status, and visible agreement or first mismatch.

© 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-replay of NVIDIA/skills.

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

Open the folder on GitHubat commit 14a98ae

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

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I4h Workflow Dataset Replay compared with similar skills
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I4h Workflow Dataset Replay this skillNVIDIA/skills3.6k1 repos~972Automated safety check: PassApache-2.0
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Memtrace Episode Replaysyncable-dev/memtrace-public489—~973Automated safety check: PassCustom licence
Browser Replayruvnet/ruflo74k—~856Automated safety check: NotesMIT
Dataset Curationwshobson/agents40k—~2kAutomated safety check: PassMIT
Arize Datasetgithub/awesome-copilot40k1 repos~3.9kAutomated safety check: NotesMIT

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Questions about I4h Workflow Dataset Replay

What does I4h Workflow Dataset Replay do?

Replay a workflow HDF5 episode through its original Scene. An agent skill from NVIDIA/skills. I4h Workflow Dataset Replay is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Replay a workflow HDF5 episode through its original Scene.

When should I use I4h Workflow Dataset Replay?

I4h Workflow Dataset Replay fits situations like: visual trajectory and recording verification; do not use for policy evaluation.

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

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

How do I install I4h Workflow Dataset Replay in Codex?

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

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

What does I4h Workflow Dataset Replay need to run?

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

Does I4h Workflow Dataset Replay 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 Replay 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 Replay use?

I4h Workflow Dataset Replay 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 Replay use?

About 972 tokens (SKILL.md is roughly 3.9k 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 Replay?

Skills that share tags, products or a category with I4h Workflow Dataset Replay: Datasets (Arize-ai/phoenix, 12k stars), Memtrace Episode Replay (syncable-dev/memtrace-public, 489 stars), Browser Replay (ruvnet/ruflo, 74k stars) and Dataset Curation (wshobson/agents, 40k 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 Replay?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,555 GitHub stars. The repository holds 390 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.