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Official agent skill

I4h Workflow Dataset Annotate

by NVIDIA in NVIDIA/skills

Grade or filter workflow HDF5 episodes with an OpenAI-compatible vision model.

OfficialApache-2.0Auto-check passedAI & LLM Engineering

Install I4h Workflow Dataset Annotate

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

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

GitHub CLI
$ gh skill install NVIDIA/skills i4h-workflow-dataset-annotate --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-annotate .claude/skills/i4h-workflow-dataset-annotate && 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-annotate
GitHub stars
3.5k
Used in
1 other repo
Token cost
~1.5k tokens
SKILL.md length
489 words
Files
5
Skills in repo
380
Repo updated
First seen
Licence
Apache-2.0

At a glance

Grade or filter workflow HDF5 episodes with an OpenAI-compatible vision model.

  • Works in 4 steps: Run the checkout resolver and select one… → Test camera sampling and the vision… → Run grading and optional filtering on… → …
  • Visual success labels
  • SKILL.md covers Purpose, Instructions, Resolve input and criterion and Resolve the endpoint, plus 8 more sections
  • Calls uv and git; reaches github.com; needs I4H_AGENT_VL_API_KEY and I4H_AGENT_API_KEY

What it does

I4h Workflow Dataset Annotate is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Grade or filter workflow HDF5 episodes with an OpenAI-compatible vision model. Use for visual success labels; do not use for replay, policy evaluation, or recordings without frames.

Its SKILL.md is about 1.5k 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`).

It sits in AI & LLM Engineering, covering Computer vision. It works with OpenAI. 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 success labels
  • Do not use for replay
  • Policy evaluation
  • Recordings without frames

Example prompts

  • “/i4h-workflow-dataset-annotate”

Requirements

  • A credential in I4H_AGENT_VL_API_KEY
  • A credential in I4H_AGENT_API_KEY

Workflow steps

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

  1. Run the checkout resolver and select one HDF5 and one criterion.
  2. Test camera sampling and the vision endpoint.
  3. Run grading and optional filtering on every selected episode.
  4. Compare verdict and output counts.

What it can do on your machine

Read from SKILL.md and the folder at commit 0e0d506. 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:

    • uv
    • git

    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 these keys or tokens, usually read from environment variables:

    • I4H_AGENT_VL_API_KEY
    • I4H_AGENT_API_KEY
    • OPENAI_API_KEY

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

Context cost

I4h Workflow Dataset Annotate loads about 1.5k tokens when it runs. Until then it costs about 53 tokens; SKILL.md has 489 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
~1.5k

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 0e0d506, republished under its Apache-2.0 licence (© NVIDIA). 489 words, ~1,472 tokens.

Download SKILL.mdSave it as .claude/skills/i4h-workflow-dataset-annotate/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-annotate
description
Grade or filter workflow HDF5 episodes with an OpenAI-compatible vision model. Use for visual success labels; do not use for replay, policy evaluation, or recordings without frames.
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, annotation, vlm

Annotate Workflow Recordings

Purpose

Grade sampled camera frames against a natural-language success criterion while keeping VLM labels separate from simulator success.

Instructions

  1. Run the checkout resolver and select one HDF5 and one criterion.
  2. Test camera sampling and the vision endpoint.
  3. Run grading and optional filtering on every selected episode.
  4. Compare verdict and output counts.

Resolve input and criterion

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 the explicit/current-chain HDF5. “All recorded episodes” means every episode in that selected file, not every historical run. Inspect it and use the user's explicit success criterion when supplied; otherwise combine the source Scene manifest instruction with the workflow's visible terminal goal semantics. Phrase placement success as the object reaching and remaining at its target, not as the robot continuing to hold it.

Resolve the endpoint

Use a caller-provided OpenAI-compatible vision endpoint/model first. Local Agent exposes that configuration as I4H_AGENT_VL_BASE_URL, I4H_AGENT_VL_MODEL, and either I4H_AGENT_VL_API_KEY or I4H_AGENT_API_KEY. Map those generic agent variables to the annotator without printing the credential:

bash
VLM_ARGS=()
if [ -n "${I4H_AGENT_VL_BASE_URL:-}" ] && [ -n "${I4H_AGENT_VL_MODEL:-}" ]; then
  I4H_VLM_URL="${I4H_AGENT_VL_BASE_URL%/}"
  case "$I4H_VLM_URL" in */v1) ;; *) I4H_VLM_URL="$I4H_VLM_URL/v1" ;; esac
  export I4H_VLM_URL
  export OPENAI_API_KEY="${I4H_AGENT_VL_API_KEY:-${I4H_AGENT_API_KEY:-EMPTY}}"
  VLM_ARGS=(--model "$I4H_AGENT_VL_MODEL")
fi

If no caller-provided endpoint/model is available, start the repository's local service:

bash
tools/annotator/scripts/vllm.sh ensure

Record whether this invocation started it. Do not hard-code a model name in the skill; use the CLI/service defaults unless the user supplies one.

Dry-run sampling when needed

bash
uv run --project tools/annotator i4h-annotator \
  --task "<success criterion>" \
  --dry-run \
  offline /absolute/path/to/recording.hdf5

Use this to verify cameras and sampled frames without transmitting images.

Show full SKILL.md (221 more words)Show less

Grade and filter

bash
RUN_DIR="$(pwd)/runs/<workflow>/$(date +%Y%m%d_%H%M%S)"
mkdir -p "$RUN_DIR"
uv run --project tools/annotator i4h-annotator \
  --task "<success criterion>" \
  "${VLM_ARGS[@]}" \
  offline /absolute/path/to/recording.hdf5 \
  --write

Add global --base-url, --model, --camera, or --frames only when selected. Add offline --node only for a requested segment. Add --filter "$RUN_DIR/filtered.hdf5" only when filtering was requested; a summarize-only prompt must grade all episodes without requiring at least one success. Keep credentials in environment variables; never print them.

Stop the local VLM only if this invocation started it:

bash
tools/annotator/scripts/vllm.sh stop

Verify

Inspect the annotator summary. If filtering was requested, also inspect the filtered file:

bash
uv run --project tools/dataset i4h-dataset inspect "$RUN_DIR/filtered.hdf5" --segments

Require a verdict for every selected episode and reconcile pass/fail counts plus filtered counts when applicable. Treat endpoint errors, absent cameras, partial writes, and unexplained zero-episode output as failure. An all-failure verdict set is a valid completed grading run for summarize-only prompts; it is not a valid filtered dataset.

Troubleshooting

Check camera sampling before endpoint/authentication errors. Never accept partial writes or a filtered file with an unexplained zero count.

Prerequisites

Require a readable workflow HDF5 with camera frames and, unless dry-running, a reachable OpenAI-compatible vision endpoint.

Limitations

Visual grading cannot recover missing frames or prove simulator state that is not visible.

Examples

  • Run annotation on all recorded episodes and summarize. → select the current HDF5, grade every episode, verify the filtered file, and report pass/fail counts.

Completion gate

Report source HDF5, selected criterion/camera/model/endpoint origin, graded pass/fail counts, filtered path/count when requested, dry-run result if used, and local-service cleanup.

© 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-annotate of NVIDIA/skills.

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

Open the folder on GitHubat commit 0e0d506

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.

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Works with

Categories

Questions about I4h Workflow Dataset Annotate

What does I4h Workflow Dataset Annotate do?

Grade or filter workflow HDF5 episodes with an OpenAI-compatible vision model. I4h Workflow Dataset Annotate is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Grade or filter workflow HDF5 episodes with an OpenAI-compatible vision model.

When should I use I4h Workflow Dataset Annotate?

I4h Workflow Dataset Annotate fits situations like: visual success labels; do not use for replay; policy evaluation; recordings without frames.

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

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

How do I install I4h Workflow Dataset Annotate in Codex?

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

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

What does I4h Workflow Dataset Annotate need to run?

Going by SKILL.md and its folder, I4h Workflow Dataset Annotate needs the command-line tools its instructions call (uv and git) and credentials named I4H_AGENT_VL_API_KEY, I4H_AGENT_API_KEY and OPENAI_API_KEY. Our summary lists: A credential in I4H_AGENT_VL_API_KEY; A credential in I4H_AGENT_API_KEY.

Does I4h Workflow Dataset Annotate 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 Annotate 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 Annotate use?

I4h Workflow Dataset Annotate 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 Annotate use?

About 1.5k tokens (SKILL.md is roughly 5.9k 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 Annotate?

Skills that share tags, products or a category with I4h Workflow Dataset Annotate: CLIP Image-Text Matching (Orchestra-Research/AI-Research-SKILLs, 13k stars), Openai Vision (benchflow-ai/skillsbench, 1.8k stars), Vision (aiskillstore/marketplace, 430 stars) and ModLens Image Vision Bridge (liustack/modlens, 4.1k 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 Annotate?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,534 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.