Converting Recorders To Services
quarkusio/quarkus
Step-by-step guide for converting Quarkus extensions from the legacy @Record/@Recorder pattern to the ServiceRegistrar service system.
Convert workflow HDF5 recordings to LeRobot datasets for training or browser inspection.
$ npx skills add NVIDIA/skills --skill i4h-workflow-dataset-convert -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills i4h-workflow-dataset-convert --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-convert .claude/skills/i4h-workflow-dataset-convert && 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-convert" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/i4h-workflow-dataset-convert into .claude/skills/i4h-workflow-dataset-convert/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "i4h-workflow-dataset-convert", 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-convertType 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-convert -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills i4h-workflow-dataset-convert --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-convert .agents/skills/i4h-workflow-dataset-convert && 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-convert" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/i4h-workflow-dataset-convert into .agents/skills/i4h-workflow-dataset-convert/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "i4h-workflow-dataset-convert", 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-convert -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills i4h-workflow-dataset-convert --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-convert .cursor/skills/i4h-workflow-dataset-convert && 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-convert" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/i4h-workflow-dataset-convert into .cursor/skills/i4h-workflow-dataset-convert/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "i4h-workflow-dataset-convert", 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-convert--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-convert -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills i4h-workflow-dataset-convert --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-convert .gemini/skills/i4h-workflow-dataset-convert && 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-convert" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/i4h-workflow-dataset-convert into .gemini/skills/i4h-workflow-dataset-convert/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "i4h-workflow-dataset-convert", 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-convertInstalls 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-convert -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-convert .github/skills/i4h-workflow-dataset-convert && 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-convert" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/i4h-workflow-dataset-convert into .github/skills/i4h-workflow-dataset-convert/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "i4h-workflow-dataset-convert", 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-convert -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-convert --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-convert .opencode/skills/i4h-workflow-dataset-convert && 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-convert" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/i4h-workflow-dataset-convert into .opencode/skills/i4h-workflow-dataset-convert/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "i4h-workflow-dataset-convert", 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-convertConvert workflow HDF5 recordings to LeRobot datasets for training or browser inspection.
I4h Workflow Dataset Convert is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Convert workflow HDF5 recordings to LeRobot datasets for training or browser inspection. Use for conversion; do not use for replay, augmentation, or raw-data repair.
Its SKILL.md is about 1.3k 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 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:
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 Convert loads about 1.3k tokens when it runs. Until then it costs about 49 tokens; SKILL.md has 460 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). 460 words, ~1,330 tokens.
.claude/skills/i4h-workflow-dataset-convert/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.Preserve recorded actions, state, cameras, task text, and embodiment labels in a local LeRobot dataset.
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 "$ROOT"
HDF5_PATH=/absolute/path/to/recording.hdf5
uv run --project tools/dataset i4h-dataset inspect "$HDF5_PATH" --segmentsTreat 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 recording. Resolve its workflow and Scene from recording metadata/context, then read the Scene manifest for the embodiment and instruction. Use the embodiment manifest for labels. Do not assume state width equals action width; the converter derives both from the recording.
RUN_DIR="$(pwd)/runs/<workflow>/$(date +%Y%m%d_%H%M%S)"
DATASET_DIR="$RUN_DIR/lerobot/local/<name>"
mkdir -p "$(dirname "$DATASET_DIR")"
[ ! -e "$DATASET_DIR" ] || { echo "Destination already exists: $DATASET_DIR" >&2; exit 2; }
uv run --project tools/dataset i4h-dataset convert \
"$HDF5_PATH" "$DATASET_DIR" \
--robot <embodiment> \
--repo-id "local/<name>" \
--successful-only \
--task "<instruction>"Use --fps or --skip-frames only when the user requests it or source metadata justifies it. Keep the default H.264 video codec for compatibility with GR00T's fast decord loader; select another --video-codec only when the target consumer requires it.
Conversion writes aggregate meta/stats.json for downstream policy loaders. Native G1 rule-based WBC recordings already contain 43-D state and 50-D action; the converter recognizes that contract and writes GR00T's required semantic meta/modality.json automatically. For a G1 recording made through the legacy 23-D Pink/keyboard contract and destined for a 50-D G1 WBC policy Task, add --g1-wbc-policy-actions. That explicit mapping combines the measured 43-joint state with the recorded navigation, base-height, and torso commands; require source action width 23 and state width 43.
Require:
meta/info.jsonmeta/stats.jsonmeta/modality.json when the target trainer requires semantic modality slicesFor G1, require modality metadata for both supported paths: native state=43/action=50, or explicitly mapped state=43/source-action=23/output-action=50. Treat a native 50-D dataset without meta/modality.json as incomplete.
Treat missing inputs or zero converted episodes as failure. If conversion leaves a partial destination, quarantine or remove that exact incomplete directory before retrying; never report it as usable.
On dimension errors, resolve the source workflow and embodiment again. On missing videos, confirm frames existed before conversion.
Require a readable HDF5 recording and its matching Scene plus embodiment manifests.
Conversion cannot reconstruct missing cameras, actions, state, task text, or successful episodes.
Convert my scissor pick-and-place recording into a LeRobot dataset. → resolve so101, convert successful episodes, and verify metadata, parquet, and both camera videos.Report source HDF5/workflow, embodiment, task text, source/converted/skipped counts, action/state widths, output directory/repo id, aggregate-stats/modality/parquet/video checks, and any missing modality.
© 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-convert 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 Dataset Convert 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 Convert this skillNVIDIA/skills | 3.5k | 1 repos | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| Converting Recorders To Servicesquarkusio/quarkus | 16k | — | ~4.6k | Automated safety check: Pass | Apache-2.0 | |
| Convertremotion-dev/remotion | 62k | — | ~247 | Automated safety check: Pass | Custom licence | |
| Convert DatasetAgibotTech/genie_sim | 1.4k | — | ~1.5k | Automated safety check: Notes | MPL-2.0 | |
| Recordingcodewhale-hq/Codewhale | 41k | — | ~540 | Automated safety check: Pass | MIT | |
| Train Poseruvnet/RuView | 97k | — | ~504 | Automated safety check: Pass | MIT |
quarkusio/quarkus
Step-by-step guide for converting Quarkus extensions from the legacy @Record/@Recorder pattern to the ServiceRegistrar service system.
remotion-dev/remotion
Start the local @remotion/convert app and open it in the Codex browser.
AgibotTech/genie_sim
Convert robot trajectory datasets between formats — currently agibot v1 → LeRobot v2.1 (parquet + HEVC/PNG-encoded MP4).
codewhale-hq/Codewhale
Capture screenshots on registered computers, record on macOS or HarmonyOS, and manage saved captures.
ruvnet/RuView
Train/evaluate WiFi pose models honestly — camera-supervised (MediaPipe + CSI) and camera-free (WiFlow), always checked against the mean-pose baseline before any PCK is quoted.
Orchestra-Research/AI-Research-SKILLs
Scales PyTorch, TensorFlow and Hugging Face training from a single GPU to multi-node clusters with Ray Train, including Ray Tune sweeps and checkpoint recovery.
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.
Convert workflow HDF5 recordings to LeRobot datasets for training or browser inspection. I4h Workflow Dataset Convert is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Convert workflow HDF5 recordings to LeRobot datasets for training or browser inspection.
I4h Workflow Dataset Convert fits situations like: do not use for replay; raw-data repair.
Run `npx skills add NVIDIA/skills --skill i4h-workflow-dataset-convert -a claude-code`. Or copy the skill folder (skills/i4h-workflow-dataset-convert in NVIDIA/skills) into .claude/skills/i4h-workflow-dataset-convert in your project. Claude Code loads it when a task matches its description.
Run `npx skills add NVIDIA/skills --skill i4h-workflow-dataset-convert -a codex`. Or copy the skill folder (skills/i4h-workflow-dataset-convert in NVIDIA/skills) into .agents/skills/i4h-workflow-dataset-convert 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-convert -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-convert, .gemini/skills/i4h-workflow-dataset-convert, .github/skills/i4h-workflow-dataset-convert and .opencode/skills/i4h-workflow-dataset-convert in your project.
Going by SKILL.md and its folder, I4h Workflow Dataset Convert 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 Convert 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 5.3k 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 Convert: Converting Recorders To Services (quarkusio/quarkus, 16k stars), Convert (remotion-dev/remotion, 62k stars), Convert Dataset (AgibotTech/genie_sim, 1.4k stars) and Recording (codewhale-hq/Codewhale, 41k 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.