I4h Workflow Dataset Mimic

NVIDIAOfficialCreated by Isaac for Healthcare TeamDistributed by NVIDIA

Skill
Workflow
Data and AI
Apache-2.0
Medium risk

From the author

Expand workflow HDF5 demonstrations with action jitter, optionally scoped to node segments. Use for synthetic variants; do not use to collect data, alter state directly, or generate new images.

— 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 workflow for augmenting existing HDF5 demonstration datasets used in healthcare robotics workflows. It enables users to create synthetic variants of successful episodes by applying controlled action jitter, which can be scoped to specific node segments. The process involves inspecting source data, generating new episodes with configurable noise levels, and verifying the integrity of the resulting dataset.

What it does

Inspects HDF5 demonstration files to identify successful episodes and segments.
Generates synthetic dataset variants by applying action jitter to source demonstrations.
Supports node-scoped jitter application for targeted data augmentation.
Verifies generated datasets for consistency in dimensions and success flags.
Integrates with visualization tools for post-generation review.

When to use it

When you need to increase the volume of demonstration data for training models.
When you want to introduce controlled variability into existing successful robot episodes.
When you need to test model robustness by adding noise to specific action nodes.
When preparing datasets for further visualization or analysis workflows.

Advantages

Automates the creation of synthetic training data from existing successful runs.
Allows for precise control over noise levels and random seeds.
Supports targeted augmentation via node-specific segment scoping.
Includes built-in verification steps to ensure data integrity.

Usage examples

“Mimic 3 more episodes and visualize my dataset.”: The agent generates three synthetic variants of the source HDF5 file with action jitter, verifies the output, converts the data, and launches the LeRobot visualizer.
“Create 10 new episodes from my source file with 0.05 noise.”: The agent creates 10 additional episodes in a new directory using the specified noise level and reports the final dataset counts.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/i4h-workflow-dataset-mimic/download

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

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

Documentation by Isaac for Healthcare Team

Mimic Workflow Demonstrations

Purpose

Clone successful source episodes and perturb recorded actions.

Instructions

Run the checkout resolver and select the source HDF5.
Inspect successful episodes and segments.
Run mimic to generate the requested additional action-jitter variants.
Compare exact counts and preserved contracts.

Resolve and inspect

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 "$ROOT"
HDF5_PATH=/absolute/path/to/source.hdf5
uv run --project tools/dataset i4h-dataset inspect "$HDF5_PATH" --segments

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 the explicit/current-chain recording. Require at least one successful source episode. If the user asks for node-scoped jitter, require that node in segment metadata.

Expand

RUN_DIR="$(pwd)/runs/<workflow>/$(date +%Y%m%d_%H%M%S)"
mkdir -p "$RUN_DIR"
uv run --project tools/mimic i4h-mimic \
  "$HDF5_PATH" "$RUN_DIR/expanded.hdf5" \
  --episodes <additional-count> \
  --noise 0.01 \
  --seed <seed> \
  --include-source \
  --successful-only

--episodes is the number of generated variants, not the final total. Add --node <node-id> only when requested. Preserve the default deterministic seed unless the user supplies another.

Verify and compose

uv run --project tools/dataset i4h-dataset inspect "$RUN_DIR/expanded.hdf5" --segments

Confirm source count, generated count, dimensions, success flags, and segment preservation. Reject zero generated episodes.

If the same prompt requests visualization, continue with i4h-workflow-dataset-convert, then i4h-lerobot-viz; raw HDF5 is not a LeRobot visualizer input.

Troubleshooting

On failure, inspect the first episode shape or segment error. If a requested node is absent, report available node ids.

Prerequisites

Require a readable HDF5 file with at least one successful episode and valid segments for node-scoped jitter.

Limitations

The current tool does not independently add state noise, rerun physics, or synthesize camera frames.

Examples

Mimic 3 more episodes and visualize my dataset. → generate three variants, convert the expanded HDF5, then launch LeRobot visualization.

Completion gate

Report input/output paths, source and generated counts, final total, action-noise value, seed, optional node, limitations, and conversion/visualizer result when requested.

Files

ATTRIBUTION.mdAdded by AutoGineer955 B
BENCHMARK.md6.8 KiB
LICENSELicence file added by AutoGineer11.2 KiB
SKILL.md3.8 KiB
evals/evals.json1.3 KiB
skill-card.md4.4 KiB
skill.oms.sig4.5 KiB

skill-card.md

Description:

Expand workflow HDF5 demonstrations with action jitter, optionally scoped to node segments. Use for synthetic variants; do not use to collect data, alter state directly, or generate new images.

This skill is ready for commercial/non-commercial use.

Owner

NVIDIA

License/Terms of Use:

Apache-2.0

Use Case:

Developers and engineers expanding Isaac for Healthcare workflow demonstration datasets with synthetic action-jitter variants for training and evaluation.

Deployment Geography for Use:

Global

Requirements / Dependencies:

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

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

Known Risks and Mitigations:

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

Reference(s):

Isaac for Healthcare Workflows Repository

Skill Output:

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

Evaluation Agents Used:

Claude Code (aws/anthropic/bedrock-claude-opus-4-8)
Codex (openai/openai/gpt-5.5)

Evaluation Tasks:

2 evaluation tasks (2 positive), 3 attempts per task, each in an isolated k8s-sandbox pod.

Evaluation Metrics Used:

Reported benchmark dimensions:

Security: Whether the skill is safe to use (unsafe operations, secret leakage, unauthorized access).
Correctness: Whether the answer is correct against the reference answer.
Discoverability: Whether the right skill was selected, decoys avoided, and workflow executed.
Effectiveness: Whether the skill helped complete the user's goal and expected workflow (goal_accuracy 50% + behavior_check 50%).
Efficiency: Whether the skill avoided wasted tool calls and token usage (skill_efficiency 50% + token_efficiency 50%).

Underlying evaluation signals used in this run:

security: Unsafe operations, secret leakage, and unauthorized access.
accuracy: Final-answer correctness against the reference answer.
skill_execution: Whether the expected skill was selected, decoys were avoided, and the workflow executed.
goal_accuracy: Whether the user's goal was achieved.
behavior_check: Whether the expected workflow behavior was followed.
skill_efficiency: Tool-call productivity (routing scored under Discoverability).
token_efficiency: Actual uncached prompt plus completion usage.

Evaluation Results:

Measure	Claude Code (Baseline → Skill Uplift)	Codex (Baseline → Skill Uplift)
Overall	88.1% — baseline ran, but no comparable score was available; uplift unavailable	70.3% — baseline ran, but no comparable score was available; uplift unavailable
Security	100.0% → 100.0% (±0.0 points)	83.3% → 50.0% (-33.3 points)
Correctness	8.0% → 100.0% (+92.0 points)	13.3% → 90.0% (+76.7 points)
Discoverability	97.5% — baseline ran, but no comparable score was available; uplift unavailable	89.0% — baseline ran, but no comparable score was available; uplift unavailable
Effectiveness	11.0% → 57.5% (+46.5 points)	5.8% → 35.0% (+29.2 points)
Efficiency	85.5% — baseline ran, but no comparable score was available; uplift unavailable	87.7% — baseline ran, but no comparable score was available; uplift unavailable

Skill Version(s):

0.8.0 (source: frontmatter)

Ethical Considerations:

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.

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

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, Needs Node.js

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-mimic · commit fce3cca

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE-APACHE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-09

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-mimic.zip

From the author

AutoGineer summary

What it does

When to use it

Advantages

Usage examples

Install

Documentation by Isaac for Healthcare Team

Mimic Workflow Demonstrations

Purpose

Instructions

Resolve and inspect

Expand

Verify and compose

Troubleshooting

Prerequisites

Limitations

Examples

Completion gate

Files

skill-card.md

Description: <br>

Owner

License/Terms of Use: <br>

Use Case: <br>

Deployment Geography for Use: <br>

Requirements / Dependencies: <br>

Known Risks and Mitigations: <br>

Reference(s): <br>

Skill Output: <br>

Evaluation Agents Used: <br>

Evaluation Tasks: <br>

Evaluation Metrics Used: <br>

Evaluation Results: <br>

Skill Version(s): <br>

Ethical Considerations: <br>

Requirements and compatibility

Security

Attribution and licence

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