Dev & Engineering ✓ NVIDIA · Official roboticsagentic-workflowisaaclab-arenagr00topenpisimulationpolicy-evaluation

i4h Agentic Workflow Guide

Navigate supported medical-robotics environments, policies, and workflow stages.

FollowSkills review · FSRS-2.0
Not recommended
46/ 100 5-point scale 2.3 / 5
Trust10 / 25 · 2.0/5

The skill clearly states that it only routes and orients, does not run pipeline stages, and does not request credentials or handle sensitive data. However, it may clone into $HOME/i4h-workflows without confirmation and depends on an external GitHub repository, with no least-privilege, data-flow, rollback, or pre-action confirmation guidance, so points are deducted.

Reliability8 / 20 · 2.0/5

Commands, fallback environment table, troubleshooting, and scope limits are reasonably clear, and BENCHMARK records external evaluation results. Static review did not execute the key command; behavior depends on repository, Git, and environment state, while abnormal-input coverage and failure diagnostics are limited, so the score remains conservative.

Adaptability9 / 15 · 3.0/5

The audience, trigger questions, supported environments, and next-stage routing are clear, and the skill explicitly limits itself to overview. It does not define detailed non-fit cases, does not document Chinese-language support, and core initialization may depend on GitHub reachability that can be unreliable for mainland-China users, so points are deducted.

Convention9 / 15 · 3.0/5

The skill provides a name, version 0.6.0, Apache-2.0 license, author, tags, references, limitations, skill index, and troubleshooting. It lacks separate Instructions and Examples sections, a changelog, and a clear maintenance/update path; repository license metadata is also NOASSERTION, so full marks are not justified.

Effectiveness6 / 15 · 2.0/5

It can provide orientation on environments, subprojects, skill routing, and setup recommendations; BENCHMARK reports 83% Codex correctness and 61% effectiveness. However, it is navigation-only, its supported-environment table may be a static fallback, its required output is rigid, and this file does not verify directly usable results for key paths, so points are deducted.

Verifiability4 / 10 · 2.0/5

The skill content, version, and evaluation tasks are traceable, and BENCHMARK provides limited external metrics. But the evaluation has only two tasks with one attempt each and lacks committed tests or independently reproducible evidence covering the skill's key command, so the static score is conservative.

Evidence confidence:Low Reviewed Jul 29, 2026 Reviewed revision ce70ca7f1966
The upstream repository has new commits since this review. The score still applies to the reviewed revision shown and may not cover the latest changes.
Before you use it
  • Do not run the default clone command without confirming the workspace and network permissions; verify I4H_WORKFLOWS, repository provenance, and GitHub reachability first.
  • The static supported-environment table may become stale; use the target repository's actual YAML files and --list-envs output before acting.
  • This skill only provides overview and routing; it should not be treated as an execution guide for recording, training, or deployment.
  • Add Chinese-language guidance, a mainland-China or offline access path, examples, a changelog, and a clear maintenance/update path.
See the full review method →

What does this skill do, and when should you use it?

i4h-workflow is an orientation and routing skill for the Isaac for Healthcare agentic workflow. It explains supported environments, robots, policies, and the roles of the `workflows/agentic/` subprojects. The skill covers navigation across simulation, data, training, and validation stages, but does not itself record demonstrations, train policies, or run rollouts. Hands-on work requires an available i4h-workflows checkout and workflow setup.

Resolves or locates the i4h-workflows base code; checks for workflows/agentic/; runs workflows/agentic/policy/run.sh --list-envs to enumerate environments; and uses environment YAMLs, subproject descriptions, and the Skill Index to route users to the next stage skill. If the base code is unavailable, it identifies ~/i4h-workflows as the default clone location.

  1. A medical-robotics researcher needs to check whether a target environment, robot, and policy are supported.
  2. An engineer is starting a simulation-to-policy workflow and needs to identify the relevant arena, policy, dataset, mimic, or annotator project.
  3. A user is unsure whether to begin with setup, data collection, conversion, fine-tuning, or validation.
  4. An environment-listing command fails and the user needs a fallback support list and setup troubleshooting path.

What are this skill's strengths and limitations?

Pros
  • Provides a single overview of supported environments, robots, policies, and subprojects.
  • Indexes stage skills spanning data collection, processing, training, and validation.
  • Includes a fallback environment table when environment discovery fails.
  • Clearly limits itself to orientation and routing.
Limitations
  • Does not record demonstrations, train policies, or execute rollouts itself.
  • Hands-on stages require separate setup and stage skills.
  • Requires an existing or clonable i4h-workflows checkout; detailed platform and hardware requirements are not provided in the supplied material.

How do you install this skill?

Install the skill from NVIDIA's catalog with: npx skills add nvidia/skills --skill i4h-workflow --yes. To target Codex specifically, use: npx skills add nvidia/skills --skill i4h-workflow --agent codex --yes. No other installation method is documented in the supplied material.

How do you use this skill?

After installation, prompt the agent with something like: What is the i4h workflow, what environments are supported, and where should I start? For hands-on work, prepare an i4h-workflows checkout and complete i4h-workflow-setup; the skill then uses workflows/agentic/policy/run.sh --list-envs and routes you to a stage skill matching your goal.

FAQ

Can this skill train or run a robot policy directly?
No. It routes users to the appropriate stage skill; recording, training, validation, and rollout are handled elsewhere.
Does using it require network access?
Not always. A local checkout can be reused through `I4H_WORKFLOWS`; network access is needed if the workflow must clone the repository from GitHub.
What should I do if environment listing fails?
Run `i4h-workflow-setup` first. If listing still fails, use the fallback supported-environments table supplied by the skill.

More skills from this repository

All from NVIDIA/skills

Dev & Engineering ✓ NVIDIA · Official

I4H Policy Fine-Tuning

Fine-tune GR00T or openpi PI0 policies on recorded LeRobot demonstrations.

Dev & Engineering ✓ NVIDIA · Official

Isaac for Healthcare Dataset Replay

Replay HDF5 episodes in Isaac Sim to verify recorded robot behavior visually.

Dev & Engineering ✓ NVIDIA · Official

I4H Workflow Environment Creator

Scaffold a runnable Isaac for Healthcare environment by forking a proven existing environment.

Automation & Ops ✓ NVIDIA · Official

Isaac for Healthcare E2E Workflow

Run the Isaac for Healthcare pipeline from recording through validation.

Automation & Ops ✓ NVIDIA · Official

I4H Agentic Workflow Setup

Checks host prerequisites and bootstraps NVIDIA’s Isaac for Healthcare agentic workflow.

Data & Analysis ✓ NVIDIA · Official

I4H Teleoperation Dataset Recorder

Record human demonstrations from keyboard, SO-ARM leader, or VR teleoperation into HDF5.

Data & Analysis ✓ NVIDIA · Official

I4H Dataset Converter

Convert Isaac for Healthcare HDF5 recordings into training-ready LeRobot datasets.

Dev & Engineering ✓ NVIDIA · Official

Isaac for Healthcare Workflow Validator

Run policy or state-machine rollouts and record verification episodes to HDF5.

Data & Analysis ✓ NVIDIA · Official

Nemotron Retrieval Recipes

Plan, debug, tune, evaluate, export, and deploy Nemotron embedding and reranking recipes.

Automation & Ops ✓ NVIDIA · Official

TAO Workflow Launch Gate

Preflight TAO jobs across platforms, credentials, data, containers, and monitoring.

Dev & Engineering ✓ NVIDIA · Official

NeMo Evaluator Plugin Skill

Run and manage NeMo Platform model evaluations through the Evaluator CLI and Python SDK.

Data & Analysis ✓ NVIDIA · Official

Nemotron Customization Pipelines

Plan, configure, and chain existing Nemotron steps for data preparation, training, alignment, conversion, optimization, and evaluation.

Data & Analysis ✓ NVIDIA · Official

Nemotron Speech ASR Customization Orchestrator

Chooses and coordinates the lowest-cost path for adapting speech recognition to a domain or language.

Data & Analysis ✓ NVIDIA · Official

Clinical ASR Evaluation and KER Leaderboard

Evaluate clinical ASR manifests and isolate terminology-recognition failures.

Dev & Engineering ✓ NVIDIA · Official

NVIDIA NuRec Neural Reconstruction Router

Routes NuRec requests to the right upstream workflow skill.

Dev & Engineering ✓ NVIDIA · Official

CUDA-Q Quantum Onboarding

Guides developers from CUDA-Q installation to quantum kernels, GPU simulation, and real QPU execution.

Dev & Engineering ✓ NVIDIA · Official

Jetson Custom Carrier Derivation

Derive a custom Jetson carrier-board fileset from a reference devkit and wire carrier differences through a device-tree overlay.

Data & Analysis ✓ NVIDIA · Official

NeMo Data Designer Synthetic Data Skill

Build synthetic datasets and declarative data-generation pipelines from a natural-language description.

Automation & Ops ✓ NVIDIA · Official

Nemotron Safety Policy Generator

Generate deployable custom safety policies for NVIDIA Nemotron content-safety models.

Data & Analysis ✓ NVIDIA · Official

TAO Standard Training Workflow

Run a controlled TAO train, evaluate, and export workflow on labeled data.

Related skills