Orientation
thedaviddias/Front-End-Checklist
A skill your agent uses when reviewing mobile-first layouts, tablet experiences, fullscreen flows, forms, dashboards, or media interfaces.
Orient users to the i4h workflow runtime and route them to the correct stage skill.
$ npx skills add NVIDIA/skills --skill i4h-workflow -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills i4h-workflow --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/i4h-workflow .claude/skills/i4h-workflow && rm -rf skills-srcUse ~/.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/
Install the "i4h-workflow" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/i4h-workflow into .claude/skills/i4h-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "i4h-workflow", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/NVIDIA/skills/tree/main/skills/i4h-workflowType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add NVIDIA/skills --skill i4h-workflow -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills i4h-workflow --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/i4h-workflow .agents/skills/i4h-workflow && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "i4h-workflow" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/i4h-workflow into .agents/skills/i4h-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "i4h-workflow", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill i4h-workflow -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills i4h-workflow --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/i4h-workflow .cursor/skills/i4h-workflow && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "i4h-workflow" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/i4h-workflow into .cursor/skills/i4h-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "i4h-workflow", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/NVIDIA/skills.git --path skills/i4h-workflow--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add NVIDIA/skills --skill i4h-workflow -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills i4h-workflow --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/i4h-workflow .gemini/skills/i4h-workflow && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "i4h-workflow" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/i4h-workflow into .gemini/skills/i4h-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "i4h-workflow", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install NVIDIA/skills i4h-workflowInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add NVIDIA/skills --skill i4h-workflow -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/i4h-workflow .github/skills/i4h-workflow && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "i4h-workflow" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/i4h-workflow into .github/skills/i4h-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "i4h-workflow", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill i4h-workflow -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install NVIDIA/skills i4h-workflow --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/i4h-workflow .opencode/skills/i4h-workflow && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "i4h-workflow" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/i4h-workflow into .opencode/skills/i4h-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "i4h-workflow", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
i4h-workflowOrient users to the i4h workflow runtime and route them to the correct stage skill.
I4h Workflow is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Orient users to the i4h workflow runtime and route them to the correct stage skill. Use for architecture, support, or where-to-start questions; do not execute a known stage.
Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including reference files (for example `BENCHMARK.md`, `evals/evals.json` and `references/repo-map.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.
4 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 67a13c0. It shows what the files ask for, not the result of running them.
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.
Shell commands in SKILL.md call:
gitFrom the folder's file list and the shell code blocks in SKILL.md.
Hosts in commands or code, which the agent is likely to contact:
github.comFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
I4h Workflow loads about 1.3k tokens when it runs, and up to ~2.2k if it reads all its reference files. Until then it costs about 47 tokens; SKILL.md has 513 words of instructions outside code blocks.
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.
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.
The full file from NVIDIA/skills at commit 67a13c0, republished under its Apache-2.0 licence (© NVIDIA). 513 words, ~1,301 tokens.
.claude/skills/i4h-workflow/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.Orient the user from live repository facts, then hand execution to the narrowest stage skill.
DESIGN.md.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"Treat this resolver 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 ./DESIGN.md for architecture and skills/i4h-workflow/references/repo-map.md for ownership. Discover current support instead of copying a static table:
./run.sh listIf discovery fails because setup is incomplete, report that limitation and route to i4h-workflow-setup.
Keep the summary precise:
ctx.scene, writes ctx.act, and never advances the simulator.TaskGraph builders, and owns goal semantics. A run mode answers how that workflow should run; code and CLI use the shorter term mode.SimulationRunner alone resets, steps, renders, records, retries whole episodes, and prints run summaries.SimulationRunner validation path.Do not describe retired environment YAMLs, per-mode runners, or separate policy/Arena launchers.
| Goal | Skill |
|---|---|
| Install, sync, or repair dependencies | i4h-workflow-setup |
| Create a new workflow/environment | i4h-workflow-create |
| Edit an existing scene, camera, task, or success rule | i4h-workflow-scene-edit |
| Record demonstrations | i4h-workflow-dataset-teleop |
| Replay HDF5 | i4h-workflow-dataset-replay |
| Augment HDF5 | i4h-workflow-dataset-mimic |
| Grade/filter HDF5 with a VLM | i4h-workflow-dataset-annotate |
| Convert HDF5 to LeRobot | i4h-workflow-dataset-convert |
| Inspect LeRobot in a browser | i4h-lerobot-viz |
| Fine-tune a manifest-backed policy task | i4h-workflow-finetune |
| RL post-train a supported policy in simulation | i4h-workflow-train-rl |
| Run policy or rule-based rollouts | i4h-workflow-validate |
| Run the maintained complete pipeline | i4h-workflow-e2e |
For Stop all, do not load a stage skill. Run ./stop.sh all from the repository root and report the stopped process count.
If discovery fails, verify the resolved checkout and run setup. If a mode is absent, report it as unsupported.
Require a readable base checkout or network access to clone it.
This router does not install, author, simulate, process data, train, or evaluate.
What does the i4h workflow include, and where should I start? → inspect live support, summarize DESIGN.md, and recommend one stage skill.Answer with the live workflow/mode list, a short architecture summary, and one concrete next skill. If the requested workflow or mode is absent from run.sh list, say it is unsupported instead of inventing a command.
© 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
SKILL.md and 5 other files (references) in skills/i4h-workflow of NVIDIA/skills.
Open the folder on GitHubat commit 67a13c0
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.
I4h Workflow 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| I4h Workflow this skillNVIDIA/skills | 3.5k | 1 repos | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| Orientationthedaviddias/Front-End-Checklist | 74k | — | ~625 | Automated safety check: Pass | MIT | |
| OmniRoute Routing CLIdiegosouzapw/OmniRoute | 74k | 1 repos | ~342 | Automated safety check: Pass | MIT | |
| OmniRoute Combo Routingdiegosouzapw/OmniRoute | 74k | — | ~2.1k | Automated safety check: Pass | MIT | |
| Intelligence Routeruvnet/ruflo | 74k | — | ~874 | Automated safety check: Notes | MIT | |
| OrientDrCatHicks/learning-opportunities | 2.5k | — | ~3.1k | Automated safety check: Notes | CC-BY-4.0 |
thedaviddias/Front-End-Checklist
A skill your agent uses when reviewing mobile-first layouts, tablet experiences, fullscreen flows, forms, dashboards, or media interfaces.
diegosouzapw/OmniRoute
Creates, switches, and inspects OmniRoute model-routing combos, plus a suggestion command with cost and latency constraints.
diegosouzapw/OmniRoute
Manages OmniRoute routing combos through its REST API: create and update combos, choose from 19 strategies, set fallback chains, test outcomes and read metrics.
ruvnet/ruflo
Route tasks via the 3-tier model selector and learned patterns; emits a routing rationale via hooksexplain
DrCatHicks/learning-opportunities
Generates a repo-specific orientation.md resource for the learning-opportunities skill.
lobehub/lobehub
Explains LobeHub's split between src/routes page segments and src/features domain code, and where router config, redirects and platform adapters belong.
NVIDIA/skills
A skill your agent uses when the user wants to deploy, run, debug, tear down, or call the REST API of the RTVI-CV 2D detection / tracking microservice.
NVIDIA/skills
Generates, validates, compares and explains HOLOLINK_def.svh macro files for the HSB IP, using bundled Python scripts and asking before it writes anything.
NVIDIA/skills
Runs and validates an end-to-end Mission Control demo in a locally installed Isaac Sim, with a Nova Carter robot driven through a Python server.
NVIDIA/skills
Orchestrates defect image generation for PCBA, metal surface and glass inspection with NVIDIA Cosmos AnomalyGen on OSMO, from cold-start Day 0 to real-photo Day 1 labeling.
NVIDIA/skills
Orchestrates video data augmentation and auto-labeling workflows on OSMO, from flow selection and preflight checks to submission, monitoring and output download.
NVIDIA/skills
Runs NVIDIA TAO Data Services KPI analysis on object detection results, comparing predictions to ground truth and writing per-class precision, recall and AP to a CSV.
Orient users to the i4h workflow runtime and route them to the correct stage skill. I4h Workflow is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Orient users to the i4h workflow runtime and route them to the correct stage skill.
I4h Workflow fits situations like: where-to-start questions; do not execute a known stage.
Run `npx skills add NVIDIA/skills --skill i4h-workflow -a claude-code`. Or copy the skill folder (skills/i4h-workflow in NVIDIA/skills) into .claude/skills/i4h-workflow in your project. Claude Code loads it when a task matches its description.
Run `npx skills add NVIDIA/skills --skill i4h-workflow -a codex`. Or copy the skill folder (skills/i4h-workflow in NVIDIA/skills) into .agents/skills/i4h-workflow in your project. Codex loads it when a task matches its description.
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 -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, .gemini/skills/i4h-workflow, .github/skills/i4h-workflow and .opencode/skills/i4h-workflow in your project.
Going by SKILL.md and its folder, I4h Workflow needs the command-line tools its instructions call (git). Our summary lists: Python 3.
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.
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.
I4h Workflow 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.
About 1.3k tokens (SKILL.md is roughly 5.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 903 tokens, read only when the agent opens those files.
Skills that share tags, products or a category with I4h Workflow: Orientation (thedaviddias/Front-End-Checklist, 74k stars), OmniRoute Routing CLI (diegosouzapw/OmniRoute, 74k stars), OmniRoute Combo Routing (diegosouzapw/OmniRoute, 74k stars) and Intelligence Route (ruvnet/ruflo, 74k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
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.