Datasets
Arize-ai/phoenix
Understand what a Phoenix dataset is and reason well about its examples, outputs, splits, and how it feeds evaluators and experiments.
Replay a workflow HDF5 episode through its original Scene. An agent skill from NVIDIA/skills.
$ npx skills add NVIDIA/skills --skill i4h-workflow-dataset-replay -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills i4h-workflow-dataset-replay --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-replay .claude/skills/i4h-workflow-dataset-replay && 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-replay" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/i4h-workflow-dataset-replay into .claude/skills/i4h-workflow-dataset-replay/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "i4h-workflow-dataset-replay", 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-replayType 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-replay -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills i4h-workflow-dataset-replay --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-replay .agents/skills/i4h-workflow-dataset-replay && 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-replay" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/i4h-workflow-dataset-replay into .agents/skills/i4h-workflow-dataset-replay/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "i4h-workflow-dataset-replay", 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-replay -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills i4h-workflow-dataset-replay --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-replay .cursor/skills/i4h-workflow-dataset-replay && 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-replay" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/i4h-workflow-dataset-replay into .cursor/skills/i4h-workflow-dataset-replay/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "i4h-workflow-dataset-replay", 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-replay--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-replay -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills i4h-workflow-dataset-replay --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-replay .gemini/skills/i4h-workflow-dataset-replay && 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-replay" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/i4h-workflow-dataset-replay into .gemini/skills/i4h-workflow-dataset-replay/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "i4h-workflow-dataset-replay", 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-replayInstalls 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-replay -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-replay .github/skills/i4h-workflow-dataset-replay && 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-replay" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/i4h-workflow-dataset-replay into .github/skills/i4h-workflow-dataset-replay/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "i4h-workflow-dataset-replay", 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-replay -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-replay --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-replay .opencode/skills/i4h-workflow-dataset-replay && 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-replay" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/i4h-workflow-dataset-replay into .opencode/skills/i4h-workflow-dataset-replay/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "i4h-workflow-dataset-replay", 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-replayReplay a workflow HDF5 episode through its original Scene. An agent skill from NVIDIA/skills.
I4h Workflow Dataset Replay is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Replay a workflow HDF5 episode through its original Scene. Use for visual trajectory and recording verification; do not use for policy evaluation or LeRobot data.
Its SKILL.md is about 970 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 14a98ae. 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 Replay loads about 972 tokens when it runs. Until then it costs about 48 tokens; SKILL.md has 333 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 14a98ae, republished under its Apache-2.0 licence (© NVIDIA). 333 words, ~972 tokens.
.claude/skills/i4h-workflow-dataset-replay/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.Replay the exact recorded action sequence through the matching workflow and inspect it visibly.
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 "$ROOT"
find runs -name '*.hdf5' -type f -printf '%T@ %p\n' | sort -nr | headTreat the resolver above as part of the skill contract: a hosted copy may run outside the base repository, so never assume the current checkout contains workflows/i4h_workflows. I4H_WORKFLOWS_REPO_URL selects the clone source. When I4H_WORKFLOWS is unset, derive the fallback directory from that URL; set I4H_WORKFLOWS only to reuse or choose a specific destination. Never replace an existing checkout.
Use an explicit or current-chain recording first. Otherwise select the newest plausible HDF5 and state that choice. Never substitute an older file after a failed recording.
Interpret natural ordinals as zero-based indices: first is 0, second is 1.
HDF5_PATH=/absolute/path/to/recording.hdf5
uv run --project tools/dataset i4h-dataset inspect "$HDF5_PATH" --segmentsResolve the original workflow from recording metadata and conversation context. Confirm the requested episode exists and its action width matches the workflow's replay Scene contract.
./run.sh <workflow> --replay "$HDF5_PATH" --episode <zero-based-index>Keep the visible simulator and command in the foreground. Poll yielded execution until run.sh exits; do not detach or return while replay is still running.
Observe the complete trajectory, relevant objects, camera views, segment boundaries, and final status. If motion diverges, report the first mismatching segment or action-contract error. Do not change workflow, episode, or recording silently.
On launch failure, verify workflow metadata, episode existence, and action width. On divergence, report the first mismatching segment.
Require the original workflow assets and a readable episode whose action width matches replay mode.
Replay verifies stored actions in one Scene; it does not evaluate a learned policy or guarantee transfer to another Scene.
Replay the second episode. → select the current recording, map “second” to index 1, and observe the visible replay.Report workflow, HDF5 path, episode index, frame/segment count, action width, exit/final status, and visible agreement or first mismatch.
© 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-replay of NVIDIA/skills.
Open the folder on GitHubat commit 14a98ae
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 Replay 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 Replay this skillNVIDIA/skills | 3.6k | 1 repos | ~972 | Automated safety check: Pass | Apache-2.0 | |
| DatasetsArize-ai/phoenix | 12k | — | ~1.6k | Automated safety check: Pass | Custom licence | |
| Memtrace Episode Replaysyncable-dev/memtrace-public | 489 | — | ~973 | Automated safety check: Pass | Custom licence | |
| Browser Replayruvnet/ruflo | 74k | — | ~856 | Automated safety check: Notes | MIT | |
| Dataset Curationwshobson/agents | 40k | — | ~2k | Automated safety check: Pass | MIT | |
| Arize Datasetgithub/awesome-copilot | 40k | 1 repos | ~3.9k | Automated safety check: Notes | MIT |
Arize-ai/phoenix
Understand what a Phoenix dataset is and reason well about its examples, outputs, splits, and how it feeds evaluators and experiments.
syncable-dev/memtrace-public
Replay the graph diff of one episode — a single git commit or working-tree save — to inspect what it changed: added/modified/removed symbols and edges.
ruvnet/ruflo
Replay a recorded session trajectory against the same URL or a mutated variant; uses browser-selectors embedding similarity to recover from DOM drift
wshobson/agents
Prepare, format, and validate datasets for supervised fine-tuning and preference training.
github/awesome-copilot
Creates, manages, and queries Arize datasets and examples. An agent skill from github/awesome-copilot.
thedaviddias/Front-End-Checklist
A skill your agent uses when reviewing scripts, client components, bundles, or runtime behavior related to Handle cross-origin requests securely.
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.
Replay a workflow HDF5 episode through its original Scene. An agent skill from NVIDIA/skills. I4h Workflow Dataset Replay is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Replay a workflow HDF5 episode through its original Scene.
I4h Workflow Dataset Replay fits situations like: visual trajectory and recording verification; do not use for policy evaluation.
Run `npx skills add NVIDIA/skills --skill i4h-workflow-dataset-replay -a claude-code`. Or copy the skill folder (skills/i4h-workflow-dataset-replay in NVIDIA/skills) into .claude/skills/i4h-workflow-dataset-replay in your project. Claude Code loads it when a task matches its description.
Run `npx skills add NVIDIA/skills --skill i4h-workflow-dataset-replay -a codex`. Or copy the skill folder (skills/i4h-workflow-dataset-replay in NVIDIA/skills) into .agents/skills/i4h-workflow-dataset-replay 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-replay -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-replay, .gemini/skills/i4h-workflow-dataset-replay, .github/skills/i4h-workflow-dataset-replay and .opencode/skills/i4h-workflow-dataset-replay in your project.
Going by SKILL.md and its folder, I4h Workflow Dataset Replay 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 Replay 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 972 tokens (SKILL.md is roughly 3.9k 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 Replay: Datasets (Arize-ai/phoenix, 12k stars), Memtrace Episode Replay (syncable-dev/memtrace-public, 489 stars), Browser Replay (ruvnet/ruflo, 74k stars) and Dataset Curation (wshobson/agents, 40k 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 3,555 GitHub stars. The repository holds 390 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.