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I4h Workflow Dataset Replay

NVIDIAOfficialCreated by Isaac for Healthcare TeamDistributed by NVIDIA

  • Skill
  • Workflow
  • Automation and integrations
  • Apache-2.0
  • Medium risk
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From the author

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.
— the description by Isaac for Healthcare Team

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured procedure for replaying recorded action sequences from HDF5 files through their corresponding workflow scenes. It is designed for visual verification of trajectories and recording integrity, ensuring that the recorded actions align with the expected workflow contracts. The process involves resolving the necessary repository assets, inspecting the recording metadata for compatibility, and executing the replay in a visible simulation environment.

What it does

  • Resolves and clones the required workflow repository based on environment configuration.
  • Inspects HDF5 recordings to verify segment data and action-contract compatibility.
  • Executes workflow replays in a foreground simulation environment.
  • Provides a framework for observing trajectories and identifying potential motion divergences.
  • Reports final status, including frame counts and action width verification.

When to use it

  • When you need to visually verify the accuracy of a recorded workflow trajectory.
  • When checking if a specific HDF5 recording matches the action-contract of a workflow scene.
  • When debugging or auditing the integrity of recorded episode data.
  • When you need to confirm that a recorded sequence executes as expected in the original environment.

Advantages

  • Ensures strict adherence to the original workflow and scene context.
  • Provides clear visibility into the execution of stored action sequences.
  • Facilitates systematic identification of mismatches between recorded data and scene contracts.
  • Standardizes the process for inspecting and replaying dataset episodes.

Usage examples

“Replay the second episode.”
The agent selects the current recording, maps the second episode to index 1, and initiates the visible replay in the simulator.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/nvidia/i4h-workflow-dataset-replay/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip i4h-workflow-dataset-replay.zip -d .agents/skills

Documentation by Isaac for Healthcare Team

Replay a Workflow Recording

Purpose

Replay the exact recorded action sequence through the matching workflow and inspect it visibly.

Instructions

  1. Run the checkout resolver and select the exact HDF5.

  2. Read the original workflow and requested episode.

  3. Inspect action-contract compatibility.

  4. 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 index 1, 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.

Files

skill-card.md

Description: <br>

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. <br>

This skill is ready for commercial/non-commercial use. <br>

Owner

NVIDIA <br>

License/Terms of Use: <br>

Apache-2.0 <br>

Use Case: <br>

Developers and engineers who need to replay and visually verify recorded HDF5 workflow episodes through their original Isaac for Healthcare simulation Scenes. <br>

Deployment Geography for Use: <br>

Global <br>

Requirements / Dependencies: <br>

Requires API Key or External Credential: [Not Specified] <br> Credential Type(s): [None identified] <br>

Do not include secrets in prompts/logs/output; use least-privilege credentials; rotate keys as appropriate. <br>

Known Risks and Mitigations: <br>

Risk: Review before execution as proposals could introduce incorrect or misleading guidance into skills. <br> Mitigation: Review and scan skill before deployment. <br>

Reference(s): <br>

Skill Output: <br>

Output Type(s): [Shell commands, Analysis] <br> Output Format: [Markdown with inline bash code blocks] <br> Output Parameters: [1D] <br> Other Properties Related to Output: [None] <br>

Evaluation Agents Used: <br>

  • Claude Code (aws/anthropic/bedrock-claude-opus-4-8) <br>

  • Codex (openai/openai/gpt-5.5) <br>

Evaluation Tasks: <br>

2 evaluation tasks (2 positive), each with 3 attempts per task in isolated k8s-sandbox pods. <br>

Evaluation Metrics Used: <br>

Reported benchmark dimensions: <br>

  • Security: Checks unsafe operations, secret leakage, and unauthorized access. <br>

  • Correctness: Checks final-answer correctness against the reference answer. <br>

  • Discoverability: Checks whether the expected skill was selected, decoys were avoided, and the workflow executed. <br>

  • Effectiveness: Equal-weight mean of goal completion and expected workflow adherence. <br>

  • Efficiency: Checks tool-call productivity and token efficiency. <br>

Underlying evaluation signals used in this run: <br>

  • security: Unsafe operations, secret leakage, and unauthorized access. <br>

  • accuracy: Final-answer correctness against the reference answer. <br>

  • skill_execution: Whether the expected skill was selected, decoys were avoided, and the workflow executed. <br>

  • skill_efficiency: Tool-call productivity (routing scored under Discoverability). <br>

  • goal_accuracy: Whether the user's goal was achieved. <br>

  • behavior_check: Whether the expected workflow behavior was followed. <br>

  • token_efficiency: Actual uncached prompt plus completion usage. <br>

Evaluation Results: <br>

MeasureClaude Code (Baseline → Skill Uplift)Codex (Baseline → Skill Uplift)
Overall86.2%74.2%
Security100.0% → 100.0% (±0.0 points)100.0% → 75.0% (-25.0 points)
Correctness25.0% → 100.0% (+75.0 points)13.3% → 90.0% (+76.7 points)
Discoverability92.5%91.5%
Effectiveness3.1% → 50.0% (+46.9 points)6.3% → 37.5% (+31.2 points)
Efficiency88.5%77.1%

Skill Version(s): <br>

0.8.0 (source: frontmatter) <br>

Ethical Considerations: <br>

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal team to ensure this skill meets requirements for the relevant industry and use case and addresses unforeseen product misuse. <br>

(For Release on NVIDIA Platforms Only) <br> Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here. <br>

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
Isaac for Healthcare Team
Publisher
NVIDIA (Official)
Distributed by
NVIDIA
Source
NVIDIA/skills · skills/i4h-workflow-dataset-replay · commit fce3cca
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE-APACHE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Isaac for Healthcare Team or NVIDIA.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download i4h-workflow-dataset-replay.zip