Recording
codewhale-hq/Codewhale
Capture screenshots on registered computers, record on macOS or HarmonyOS, and manage saved captures.
Record demonstrations through a workflow's teleop Task into workflow HDF5.
$ npx skills add NVIDIA/skills --skill i4h-workflow-dataset-teleop -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills i4h-workflow-dataset-teleop --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-dataset-teleop .claude/skills/i4h-workflow-dataset-teleop && 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-dataset-teleop" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/i4h-workflow-dataset-teleop into .claude/skills/i4h-workflow-dataset-teleop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "i4h-workflow-dataset-teleop", 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-workflow-dataset-teleopType 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-dataset-teleop -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills i4h-workflow-dataset-teleop --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-dataset-teleop .agents/skills/i4h-workflow-dataset-teleop && 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-dataset-teleop" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/i4h-workflow-dataset-teleop into .agents/skills/i4h-workflow-dataset-teleop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "i4h-workflow-dataset-teleop", 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-dataset-teleop -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills i4h-workflow-dataset-teleop --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-dataset-teleop .cursor/skills/i4h-workflow-dataset-teleop && 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-dataset-teleop" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/i4h-workflow-dataset-teleop into .cursor/skills/i4h-workflow-dataset-teleop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "i4h-workflow-dataset-teleop", 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-dataset-teleop--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-dataset-teleop -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills i4h-workflow-dataset-teleop --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-dataset-teleop .gemini/skills/i4h-workflow-dataset-teleop && 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-dataset-teleop" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/i4h-workflow-dataset-teleop into .gemini/skills/i4h-workflow-dataset-teleop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "i4h-workflow-dataset-teleop", 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-workflow-dataset-teleopInstalls 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-dataset-teleop -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-dataset-teleop .github/skills/i4h-workflow-dataset-teleop && 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-dataset-teleop" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/i4h-workflow-dataset-teleop into .github/skills/i4h-workflow-dataset-teleop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "i4h-workflow-dataset-teleop", 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-dataset-teleop -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-dataset-teleop --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-dataset-teleop .opencode/skills/i4h-workflow-dataset-teleop && 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-dataset-teleop" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/i4h-workflow-dataset-teleop into .opencode/skills/i4h-workflow-dataset-teleop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "i4h-workflow-dataset-teleop", 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-workflow-dataset-teleopRecord demonstrations through a workflow's teleop Task into workflow HDF5.
I4h Workflow Dataset Teleop is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Record demonstrations through a workflow's teleop Task into workflow HDF5. Use for keyboard, leader, VR, or bus input; do not use for policy evaluation or autonomous rule-based Tasks.
Its SKILL.md is about 1.2k 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.
4 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit dfdd080. 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:
gituvFrom 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 Dataset Teleop loads about 1.2k tokens when it runs. Until then it costs about 53 tokens; SKILL.md has 425 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 dfdd080, republished under its Apache-2.0 licence (© NVIDIA). 425 words, ~1,184 tokens.
.claude/skills/i4h-workflow-dataset-teleop/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.Run the workflow's declared teleop graph through the shared SimulationRunner so actions, state, cameras, segments, attempts, and outcomes use the normal HDF5 contract.
run.sh.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 "$ROOT"
./run.sh list
./run.sh show <workflow> --mode teleopTreat the resolver above as part of the skill contract: a hosted copy may run outside the base repository, so never assume the current checkout contains workflows/i4h_workflows. I4H_WORKFLOWS_REPO_URL selects the clone source. When I4H_WORKFLOWS is unset, derive the fallback directory from that URL; set I4H_WORKFLOWS only to reuse or choose a specific destination. Never replace an existing checkout.
Require teleop in the live mode list. Read the workflow builder, its scene manifest teleop override, and the embodiment manifest's teleop_devices. Do not maintain a static support table in the skill.
./run.sh <workflow> --teleop <device> \
--episodes <N> --attempts 3 \
--recordOmit <device> to use the workflow default. Bare --record writes demos.hdf5 inside the launcher's automatic run directory. Read the absolute directory from the ==> run dir ... line or run.json; do not recreate its timestamp in the shell. When a larger pipeline requires a caller-selected shared directory, pass --run-dir "$RUN_DIR" --record demos.hdf5; the launcher creates the directory and anchors the relative recording name inside it. An absolute --record path remains supported.
Require the final N/N episodes succeeded summary. Then inspect content:
RUN_DIR="<absolute run_dir from run.json or launcher output>"
uv run --project tools/dataset i4h-dataset inspect "$RUN_DIR/demos.hdf5" --segments
uv run --project tools/dataset i4h-dataset actions "$RUN_DIR/demos.hdf5"Visually confirm that the operator completes the requested task, robot motion matches the input device, and all expected cameras record the same behavior. Treat zero saved episodes, missing observations, absent action motion, or an unsuccessful task outcome as failure. Stop leftovers with ./stop.sh all.
On device or width errors, compare the workflow teleop builder, Scene mode override, and embodiment devices.
Require a workflow with teleop, a supported device, a working simulator, and a present operator for interactive input.
Teleop records human input and requires an operator for interactive devices. Record autonomous rule-based Tasks with i4h-workflow-validate instead.
Record 5 keyboard teleop demonstrations for locomanip tray pick and place. → use G1's supported keyboard device, require a human operator, and verify the recorded action motion.Report workflow, mode/device, controls, requested/saved episodes, attempts, visual result, HDF5 path, dimensions/segments, and whether a human operator completed the task.
© 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 4 other files in skills/i4h-workflow-dataset-teleop of NVIDIA/skills.
Open the folder on GitHubat commit dfdd080
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 Dataset Teleop 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 Dataset Teleop this skillNVIDIA/skills | 3.5k | 1 repos | ~1.2k | Automated safety check: Pass | Apache-2.0 | |
| Recordingcodewhale-hq/Codewhale | 41k | — | ~540 | Automated safety check: Pass | MIT | |
| DatasetsArize-ai/phoenix | 12k | — | ~1.6k | Automated safety check: Pass | Custom licence | |
| Architecture Decision Recordsaffaan-m/ECC | 276k | 4 repos | ~1.8k | Automated safety check: Pass | MIT | |
| Architecture Decision Recordsaffaan-m/ECC | 276k | 1 repos | ~863 | Automated safety check: Pass | MIT | |
| Architecture Decision Recordsaffaan-m/ECC | 276k | — | ~1.1k | Automated safety check: Pass | MIT |
codewhale-hq/Codewhale
Capture screenshots on registered computers, record on macOS or HarmonyOS, and manage saved captures.
Arize-ai/phoenix
Understand what a Phoenix dataset is and reason well about its examples, outputs, splits, and how it feeds evaluators and experiments.
affaan-m/ECC
Capture architectural decisions as numbered ADR markdown files in docs/adr/ with context, alternatives considered, consequences, and an index README.
affaan-m/ECC
在Claude Code会话期间,将做出的架构决策捕获为结构化的架构决策记录(ADR)。自动检测决策时刻,记录上下文、考虑的替代方案和理由。维护一个ADR日志,以便未来的开发人员理解代码库为何以当前方式构建。
affaan-m/ECC
コーディングセッション中にアーキテクチャ決定を構造化ADRとして記録し、自動的に決定の瞬間を検出し、コンテキスト、検討された代替案、根拠を記録します。今後の開発者がコードベースの形成理由を理解するためのADRログを維持します。
ruvnet/ruflo
Open a named, traced browser session into an RVF cognitive container with a ruvector trajectory recording every action
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.
Record demonstrations through a workflow's teleop Task into workflow HDF5. I4h Workflow Dataset Teleop is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Record demonstrations through a workflow's teleop Task into workflow HDF5.
I4h Workflow Dataset Teleop fits situations like: do not use for policy evaluation; autonomous rule-based Tasks.
Run `npx skills add NVIDIA/skills --skill i4h-workflow-dataset-teleop -a claude-code`. Or copy the skill folder (skills/i4h-workflow-dataset-teleop in NVIDIA/skills) into .claude/skills/i4h-workflow-dataset-teleop in your project. Claude Code loads it when a task matches its description.
Run `npx skills add NVIDIA/skills --skill i4h-workflow-dataset-teleop -a codex`. Or copy the skill folder (skills/i4h-workflow-dataset-teleop in NVIDIA/skills) into .agents/skills/i4h-workflow-dataset-teleop 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-dataset-teleop -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-teleop, .gemini/skills/i4h-workflow-dataset-teleop, .github/skills/i4h-workflow-dataset-teleop and .opencode/skills/i4h-workflow-dataset-teleop in your project.
Going by SKILL.md and its folder, I4h Workflow Dataset Teleop needs the command-line tools its instructions call (git and uv).
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 Dataset Teleop 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 1.2k tokens (SKILL.md is roughly 4.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with I4h Workflow Dataset Teleop: Recording (codewhale-hq/Codewhale, 41k stars), Datasets (Arize-ai/phoenix, 12k stars), Architecture Decision Records (affaan-m/ECC, 276k stars) and Architecture Decision Records (affaan-m/ECC, 276k 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 3,546 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.