Official agent skill

I4h Workflow Finetune

by NVIDIA in NVIDIA/skills

Fine-tune a manifest-backed GR00T or openpi remote Task on compatible LeRobot data.

OfficialApache-2.0Auto-check passedAI & LLM Engineering

Install I4h Workflow Finetune

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

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

GitHub CLI
$ gh skill install NVIDIA/skills i4h-workflow-finetune --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-finetune .claude/skills/i4h-workflow-finetune && 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-finetune
GitHub stars
3.5k
Used in
1 other repo
Token cost
~1.5k tokens
SKILL.md length
527 words
Files
5
Skills in repo
386
Repo updated
First seen
Licence
Apache-2.0

At a glance

Fine-tune a manifest-backed GR00T or openpi remote Task on compatible LeRobot data.

  • Works in 4 steps: Resolve the base checkout, policy mode,… → Verify dataset compatibility and a train… → Dry-run the exact configuration. → …
  • Do not use for inference-only Tasks
  • SKILL.md covers Purpose, Instructions, Resolve the workflow, task,… and Resolve configuration before a…, plus 7 more sections
  • Calls uv and git; reaches github.com

What it does

I4h Workflow Finetune is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Fine-tune a manifest-backed GR00T or openpi remote Task on compatible LeRobot data. Use for training; do not use for inference-only Tasks or checkpoint rollout.

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 Fine-tuning. 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 inference-only Tasks
  • Checkpoint rollout

Example prompts

  • “/i4h-workflow-finetune”

Requirements

  • Python 3

Workflow steps

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

  1. Resolve the base checkout, policy mode, and remote task.
  2. Verify dataset compatibility and a train block.
  3. Dry-run the exact configuration.
  4. Train in the foreground and verify checkpoint artifacts.

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:

    • 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 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 Finetune loads about 1.5k tokens when it runs. Until then it costs about 46 tokens; SKILL.md has 527 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~46
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 dfdd080, republished under its Apache-2.0 licence (© NVIDIA). 527 words, ~1,463 tokens.

Download SKILL.mdSave it as .claude/skills/i4h-workflow-finetune/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
i4h-workflow-finetune
description
Fine-tune a manifest-backed GR00T or openpi remote Task on compatible LeRobot data. Use for training; do not use for inference-only Tasks or checkpoint rollout.
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, lerobot, gr00t, openpi

Fine-tune a Workflow Policy Task

Purpose

Resolve and run training from the selected workflow run mode and owning remote-task manifest.

Instructions

  1. Resolve the base checkout, policy mode, and remote task.
  2. Verify dataset compatibility and a train block.
  3. Dry-run the exact configuration.
  4. Train in the foreground and verify checkpoint artifacts.

Resolve the workflow, task, and data

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 <policy-mode>
test -f /absolute/path/to/dataset/meta/info.json
nvidia-smi

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.

Read the selected workflow run mode to identify its remote task id. Open tasks/<project>/i4h_tasks/<project>/manifest/<task>.yaml and require train:. Resolve the project, entry point, base model/config, output defaults, and modality contract from that manifest and the project's train.py.

Use the current-chain LeRobot dataset when the prompt omits a path. Verify its embodiment, cameras, task text, feature widths, and episode count are compatible with the remote task.

Resolve configuration before a long run

All policy train entry points support --dry-run:

bash
uv run --project "tasks/<project>" "i4h-tasks-<project-with-hyphens>-train" \
  --task <project>/<task> \
  --dataset /absolute/path/to/dataset \
  --output-dir /absolute/path/to/checkpoints \
  --max-steps <N> \
  --batch-size <N> \
  --dry-run

Inspect the resolved config. Keep user-requested steps, batch size, model/config, and GPU count exact.

For GR00T, “turn off vision tuning” maps to --no-tune-visual. Do not pass that flag to openpi, whose CLI does not expose it. Use only flags present in the selected project's current train.py.

Train

Remove --dry-run and keep the command in the foreground:

bash
uv run --project "tasks/<project>" "i4h-tasks-<project-with-hyphens>-train" \
  --task <project>/<task> \
  --dataset /absolute/path/to/dataset \
  --output-dir /absolute/path/to/checkpoints \
  --max-steps <N> \
  --save-steps <N> \
  --batch-size <N> \
  --num-gpus <N>

Add backend-specific flags only after resolving them. Do not silently lower requested steps or batch size to make training fit.

Show full SKILL.md (257 more words)Show less

Verify

Require exit status 0, completed requested steps, saved training logs, and at least one loadable checkpoint artifact. Resolve the exact checkpoint path rather than calling an incomplete output directory a checkpoint.

Run a bounded backend load smoke before reporting the checkpoint usable:

bash
uv run --project "tasks/<project>" python -m "<project>.server" \
  --namespace "checkpoint-smoke-$$" \
  --preload <project>/<task> \
  --checkpoint /absolute/path/to/checkpoint \
  --preload-only

Use the selected project's actual module path. --preload-only loads the manifest and checkpoint through the inference backend, then exits without starting a rollout. A training exit alone proves that files were written, not that inference can load them.

Report du -sh for the task output and the selected checkpoint. Some trainers save both a final model at the output root and numbered checkpoints; identify that duplication, but do not delete either copy unless the user explicitly asks for cleanup.

Hand the exact load-smoked checkpoint path to i4h-workflow-validate; do not evaluate unless the user requested it.

Troubleshooting

Report the first dataset, manifest, model-access, GPU-memory, or backend error. Preserve logs and never silently change requested hyperparameters.

Prerequisites

Require a compatible LeRobot dataset, synced policy environment, model access, GPU capacity, and a remote-task manifest with train:.

Limitations

Inference-only Tasks cannot be fine-tuned, and this skill does not claim rollout success from training alone.

Examples

  • Fine-tune for 200 steps with a batch size of 32. Turn off vision tuning. → preserve exact values, apply GR00T's supported vision flag, dry-run, train, and report the checkpoint.

Completion gate

Report workflow/mode, task id and manifest, dataset compatibility, resolved config, requested/completed steps, batch/GPU/vision settings, checkpoint path, bounded load-smoke result, output/checkpoint disk sizes, exact validation handoff, exit summary, and any inference-only or resource blocker.

© 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-finetune 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

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Train SftOpenPipe/ART11k—~2.9kAutomated safety check: PassApache-2.0

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Questions about I4h Workflow Finetune

What does I4h Workflow Finetune do?

Fine-tune a manifest-backed GR00T or openpi remote Task on compatible LeRobot data. I4h Workflow Finetune is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Fine-tune a manifest-backed GR00T or openpi remote Task on compatible LeRobot data.

When should I use I4h Workflow Finetune?

I4h Workflow Finetune fits situations like: do not use for inference-only Tasks; checkpoint rollout.

How do I install I4h Workflow Finetune in Claude Code?

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

How do I install I4h Workflow Finetune in Codex?

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

Can I use I4h Workflow Finetune 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-finetune -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-finetune, .gemini/skills/i4h-workflow-finetune, .github/skills/i4h-workflow-finetune and .opencode/skills/i4h-workflow-finetune in your project.

What does I4h Workflow Finetune need to run?

Going by SKILL.md and its folder, I4h Workflow Finetune needs the command-line tools its instructions call (uv and git). Our summary lists: Python 3.

Does I4h Workflow Finetune 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 Finetune 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 Finetune use?

I4h Workflow Finetune 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 Finetune 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 Finetune?

Skills that share tags, products or a category with I4h Workflow Finetune: Sentence-Transformers Training Router (huggingface/skills, 11k stars), Train Rl (OpenPipe/ART, 11k stars), Qwopus27b Rl Training (R6410418/Jackrong-llm-finetuning-guide, 1.7k stars) and Dataset Evaluation (awslabs/agent-plugins, 915 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains I4h Workflow Finetune?

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.