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I4h Workflow Dataset Convert

NVIDIAOfficialCreated by Isaac for Healthcare TeamDistributed by NVIDIA

  • Skill
  • Workflow
  • Data and AI
  • Apache-2.0
  • Medium risk
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From the author

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.
— 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 converting HDF5 recordings from the Isaac for Healthcare (i4h) framework into the LeRobot dataset format. It handles the extraction of state, actions, camera data, and task instructions, ensuring the resulting dataset is compatible with downstream training pipelines. The process includes environment resolution, data conversion, and a verification step to ensure metadata, modality files, and episode counts are correct.

What it does

  • Resolves workflow, scene, and embodiment metadata from HDF5 recordings.
  • Converts successful episodes from HDF5 format to LeRobot-compatible datasets.
  • Generates required metadata files including stats and modality mappings.
  • Supports specific action mapping for G1 robot configurations.
  • Verifies the integrity of the converted dataset, including parquet data and video files.
  • Quarantines incomplete or failed conversion attempts.

When to use it

  • When preparing recorded robot workflow data for training machine learning policies.
  • When you need to inspect recorded robot actions and states using LeRobot tools.
  • When migrating legacy G1 recordings to modern WBC policy formats.
  • When you need to ensure dataset compatibility with GR00T policy loaders.

Advantages

  • Ensures consistency between source recordings and training-ready datasets.
  • Automates the generation of essential metadata like modality and stats.
  • Provides built-in verification to catch missing data or dimension mismatches.
  • Handles complex action-space mapping for specific robot embodiments.

Usage examples

“Convert my scissor pick-and-place recording into a LeRobot dataset.”
The agent resolves the workflow and embodiment, converts successful episodes, and verifies the output metadata, parquet files, and camera videos.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/i4h-workflow-dataset-convert/download

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

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

Documentation by Isaac for Healthcare Team

Convert Workflow HDF5 to LeRobot

Purpose

Preserve recorded actions, state, cameras, task text, and embodiment labels in a local LeRobot dataset.

Instructions

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

  2. Read the workflow, Scene, embodiment, and instruction.

  3. Run conversion for the selected successful episodes.

  4. Inspect metadata, parquet, videos, and feature widths.

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

Convert

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.

Verify

Require:

  • meta/info.json

  • meta/stats.json

  • meta/modality.json when the target trainer requires semantic modality slices

  • episode parquet data

  • video files for every recorded camera

  • converted episode count matching selected successful sources

  • action/state feature widths and names matching the recording plus embodiment descriptor

For 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.

Troubleshooting

On dimension errors, resolve the source workflow and embodiment again. On missing videos, confirm frames existed before conversion.

Prerequisites

Require a readable HDF5 recording and its matching Scene plus embodiment manifests.

Limitations

Conversion cannot reconstruct missing cameras, actions, state, task text, or successful episodes.

Examples

  • Convert my scissor pick-and-place recording into a LeRobot dataset. → resolve so101, convert successful episodes, and verify metadata, parquet, and both camera videos.

Completion gate

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.

Files

BENCHMARK.md

Skill Benchmark: i4h-workflow-dataset-convert

✅ Overall verdict: PASS — Recommended for publication

Publication Recommendation

Recommended for publication based on the completed evaluation evidence in this report.

Evaluation Metadata

  • Skill: i4h-workflow-dataset-convert

  • Evaluation date: 2026-09-21

  • Evaluator version: 1.5.6

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

  • Tasks: 3 evaluation tasks (3 positive)

  • Dataset digest: sha256:c419f87fbbf11d2b03666ba2d679b84bd034853994f033900031bf092b0ce801 (skill-evaluator-dataset-snapshot/1)

  • Attempts per task: 3

  • Environment: k8s-sandbox

  • Tier 2 evidence: required for publication

  • Tier 3 evidence: required for publication

Each task attempt ran in its own isolated sandbox pod.

What This Report Answers

The three-tier evaluation checks whether the skill:

  • is safe to use;

  • produces correct answers;

  • is discovered and activated when needed;

  • helps the agent complete the user's goal and expected workflow; and

  • avoids wasted skill and tool usage.

Results at a Glance

MeasureClaude Code (Baseline → Skill Uplift)Codex (Baseline → Skill Uplift)
Overall75.2% — baseline ran, but no comparable score was available; uplift unavailable84.8% — baseline ran, but no comparable score was available; uplift unavailable
Security100.0% → 87.5% (-12.5 points)92.9% → 100.0% (+7.1 points)
Correctness37.1% → 75.0% (+37.9 points)37.1% → 100.0% (+62.9 points)
Discoverability96.3% — baseline ran, but no comparable score was available; uplift unavailable90.0% — baseline ran, but no comparable score was available; uplift unavailable
Effectiveness12.1% → 34.2% (+22.1 points)7.9% → 45.0% (+37.1 points)
Efficiency82.9% — baseline ran, but no comparable score was available; uplift unavailable88.8% — baseline ran, but no comparable score was available; uplift unavailable

How to read this table: baseline is the same task attempted without the target skill. Scores are rounded to one decimal; threshold-adjacent values use additional precision so their displayed band matches the verdict. Uplift is derived from those displayed scores and shown in percentage points.

Example: 47.0% → 92.0% (+45.0 points) means the skill-assisted run scored 92.0%, 45.0 percentage points above its 47.0% no-skill baseline.

Token Usage

Actual Tier 3 execution usage is reported for every observed agent/case pair and both conditions.

AgentDataset caseWith skillWithout skillDeltaChangeCoverage
claude-codeAll cases1,606,1821,642,564N/AN/Askill 4/4; base 7/7
claude-codei4h-workflow-dataset-convert-latest-training310,100292,664+17,436+5.96%skill 1/1; base 1/1
claude-codei4h-workflow-dataset-convert-readme-convert-and-visualize915,593654,490N/AN/Askill 2/2; base 3/3
claude-codei4h-workflow-dataset-convert-table-scissor380,489695,410N/AN/Askill 1/1; base 3/3
codexAll cases313,445901,175N/AN/Askill 3/3; base 7/7
codexi4h-workflow-dataset-convert-latest-training146,371100,417+45,954+45.76%skill 1/1; base 1/1
codexi4h-workflow-dataset-convert-readme-convert-and-visualize74,996449,088N/AN/Askill 1/1; base 3/3
codexi4h-workflow-dataset-convert-table-scissor92,078351,670N/AN/Askill 1/1; base 3/3
ALL AGENTSDataset aggregate1,919,6272,543,739N/AN/Askill 7/7; base 14/14

Prompt tokens include cached reads, so total tokens are prompt + completion (cached is not added twice). The Efficiency score uses (prompt - cached) + completion. N/A means the relevant trajectory counters were not available; coverage is never estimated.

Tier Status

TierPurposeStatusEvidence
Tier 1Static validationPASSED WITH OBSERVATIONS11 validator(s); 2 finding(s)
Tier 2Semantic deduplicationPASSED2 validator(s); 0 finding(s)
Tier 3Live agent evaluationPASS2 agent(s); 3 task(s)

Findings and Observations

<details> <summary>Show detailed findings and successful checks</summary>

  • MEDIUM SECURITY/Unknown (SQP-2): The skill instructs an automated agent to clone a remote Git repository (controlled by I4H_WORKFLOWS_REPO_URL, which d (SKILL.md:33)

  • LOW QUALITY/quality_reliability: Inputs are used but no dedicated Inputs section is documented (team-skills/holoscan/i4h-workflows/i4h-workflow-dataset-convert/SKILL.md)

</details>

Scoring Methodology

<details> <summary>Show dimension definitions, source signals, and thresholds</summary>

DimensionQuestionScored signals
SecurityIs it safe to use?security (100%)
CorrectnessIs the answer correct?accuracy (100%)
DiscoverabilityWas the right skill loaded when needed?skill_execution (100%)
EffectivenessDid the skill help complete the task?goal_accuracy (50%) + behavior_check (50%)
EfficiencyDid it avoid wasted tool calls and token usage?skill_efficiency (50%) + token_efficiency (50%)

  • Dimension bands: PASS at 50% or above; NEUTRAL from 40% to below 50%; FAIL below 40%.

  • Overall Tier 3 lift: PASS at +5 points or more; FAIL at -10 points or less; values between those bands are NEUTRAL.

  • Overall verdict: PASS only when every configured dimension passes for at least one supported agent. Lift is reported as diagnostic evidence and does not override this gate.

  • The 50% attempt pass threshold is a separate per-task gate; it is not the dimension pass threshold.

  • Effectiveness is the equal-weight mean of goal completion (goal_accuracy) and expected workflow adherence (behavior_check).

  • Efficiency is 50% tool-call productivity (the backward-compatible skill_efficiency wire id) and 50% token_efficiency. Positive-case skill routing is scored under Discoverability, not Efficiency; a negative case without a routing target is N/A. N/A sources are omitted, remaining weights are renormalized, and the dimension is marked partial.

Signals present in this run:

  • security (Security): unsafe operations, secret leakage, and unauthorized access.

  • skill_execution (Skill Execution): whether the expected skill was selected, decoys were avoided, and the workflow executed.

  • skill_efficiency (Tool Productivity): tool-call productivity (legacy wire id; routing is scored under Discoverability).

  • accuracy (Accuracy): final-answer correctness against the reference answer.

  • goal_accuracy (Goal Accuracy): whether the user's goal was achieved.

  • behavior_check (Behavior Check): whether the expected workflow behavior was followed.

  • token_efficiency (Token Efficiency): actual uncached prompt plus completion usage (50% of Efficiency).

</details>

Freshness

Regenerate this benchmark when the skill, evaluation dataset, target agent/model, evaluator version, environment, or scoring policy changes.

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-convert · 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-convert.zip