i4h-workflow-dataset-replay
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.
Install / Use
npx skills add NVIDIA/skills --skill i4h-workflow-dataset-replayInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
AutomationSupported Platforms
Tags
Our assessment of i4h-workflow-dataset-replay
i4h-workflow-dataset-replay scores 89/100 on our quality scale, 913th of 2,125 Automation skills we index (top 43%).
Its SKILL.md is 3.8 KB long, well organised into 12 sections with 3 code examples: a solid amount of guidance for an agent.
With 3,421 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated 5 days ago, so i4h-workflow-dataset-replay is actively maintained.
- It is released under the Apache-2.0 license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
i4h-workflow-dataset-replay compared with similar skills
All 4 of these similar skills score higher than i4h-workflow-dataset-replay; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| i4h-workflow-dataset-replay (this skill)by NVIDIA | 89 | 3.4k | 5d ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 86.0k | 13d ago | CLAUDE.md |
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| Scraplingby D4Vinci | 100 | 84.4k | today | MCP Server |
| algorithmic-artby anthropics | 100 | 177.9k | 6d ago | SKILL.md |
Frequently asked questions
- How do I install i4h-workflow-dataset-replay?
- Run
npx skills add NVIDIA/skills --skill i4h-workflow-dataset-replay. The install tabs above show the steps for each supported agent. - Which AI agents does i4h-workflow-dataset-replay work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is i4h-workflow-dataset-replay safe to use?
- It is Apache-2.0-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is i4h-workflow-dataset-replay still maintained?
- The repository was last updated 5 days ago, so i4h-workflow-dataset-replay is actively maintained.
Skill content
View source on GitHubname: i4h-workflow-dataset-replay description: 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. license: Apache-2.0 metadata: author: "Isaac for Healthcare Team isaac-for-healthcare-support@nvidia.com" version: "0.8.0" tags: - isaac-for-healthcare - i4h - dataset - replay - hdf5
Replay a Workflow Recording
Purpose
Replay the exact recorded action sequence through the matching workflow and inspect it visibly.
Instructions
- Run the checkout resolver and select the exact HDF5.
- Read the original workflow and requested episode.
- Inspect action-contract compatibility.
- Run the replay visibly in the foreground through completion.
Resolve the recording
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 "$ROOT"
find runs -name '*.hdf5' -type f -printf '%T@ %p\n' | sort -nr | head
Treat the resolver above as part of the skill contract: a hosted copy may run outside the base repository, so never assume the current checkout contains workflows/i4h_workflows. I4H_WORKFLOWS_REPO_URL selects the clone source. When I4H_WORKFLOWS is unset, derive the fallback directory from that URL; set I4H_WORKFLOWS only to reuse or choose a specific destination. Never replace an existing checkout.
Use an explicit or current-chain recording first. Otherwise select the newest plausible HDF5 and state that choice. Never substitute an older file after a failed recording.
Interpret natural ordinals as zero-based indices: first is 0, second is 1.
Inspect before launch
HDF5_PATH=/absolute/path/to/recording.hdf5
uv run --project tools/dataset i4h-dataset inspect "$HDF5_PATH" --segments
Resolve 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.
Replay
./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.
Verify
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.
Troubleshooting
On launch failure, verify workflow metadata, episode existence, and action width. On divergence, report the first mismatching segment.
Prerequisites
Require the original workflow assets and a readable episode whose action width matches replay mode.
Limitations
Replay verifies stored actions in one Scene; it does not evaluate a learned policy or guarantee transfer to another Scene.
Examples
Replay the second episode.→ select the current recording, map “second” to index1, and observe the visible replay.
Completion gate
Report workflow, HDF5 path, episode index, frame/segment count, action width, exit/final status, and visible agreement or first mismatch.
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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
