I4h Workflow Dataset Annotate
NVIDIAOfficialCreated by Isaac for Healthcare TeamDistributed by NVIDIA
- Skill
- Workflow
- Data and AI
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a structured process for annotating and filtering HDF5 workflow recordings within the Isaac for Healthcare ecosystem. It leverages OpenAI-compatible vision language models (VLMs) to evaluate camera frames against natural-language success criteria. The process includes environment resolution, camera sampling verification, VLM endpoint configuration, and automated grading. It is designed to generate visual success labels for datasets while ensuring that VLM-based annotations remain distinct from simulator-based success metrics.
What it does
- Resolves and prepares HDF5 workflow repositories for processing.
- Configures connections to external or local OpenAI-compatible vision endpoints.
- Performs dry-run sampling to verify camera frames without image transmission.
- Grades recorded episodes based on user-defined success criteria.
- Filters datasets to produce new HDF5 files containing only successful episodes.
- Provides summary reports of pass/fail counts and verification of filtered outputs.
When to use it
- When you need to generate visual success labels for recorded workflow episodes.
- When you need to filter large HDF5 datasets to isolate successful task executions.
- When you want to validate simulator-based outcomes against visual evidence.
- When working with Isaac for Healthcare workflow recordings that contain camera frames.
Advantages
- Enables automated, large-scale annotation of visual data.
- Supports both external cloud-based and local VLM service endpoints.
- Includes dry-run capabilities to prevent unnecessary API costs or processing.
- Maintains clear separation between VLM-derived labels and simulator state.
Usage examples
- “Run annotation on all recorded episodes and summarize.”
- The agent selects the current HDF5 file, grades every episode against the success criteria, verifies the output, and reports the final pass/fail counts.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/i4h-workflow-dataset-annotate/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip i4h-workflow-dataset-annotate.zip -d .agents/skills
Documentation by Isaac for Healthcare Team
Annotate Workflow Recordings
Purpose
Grade sampled camera frames against a natural-language success criterion while keeping VLM labels separate from simulator success.
Instructions
Run the checkout resolver and select one HDF5 and one criterion.
Test camera sampling and the vision endpoint.
Run grading and optional filtering on every selected episode.
Compare verdict and output counts.
Resolve input and criterion
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 "$ROOT"
find runs -name '*.hdf5' -type f -printf '%T@ %p\n' | sort -nr | headTreat the resolver above as part of the skill contract: a hosted copy may run outside the base repository, so never assume the current checkout contains workflows/i4h_workflows. I4H_WORKFLOWS_REPO_URL selects the clone source. When I4H_WORKFLOWS is unset, derive the fallback directory from that URL; set I4H_WORKFLOWS only to reuse or choose a specific destination. Never replace an existing checkout.
Use the explicit/current-chain HDF5. “All recorded episodes” means every episode in that selected file, not every historical run. Inspect it and use the user's explicit success criterion when supplied; otherwise combine the source Scene manifest instruction with the workflow's visible terminal goal semantics. Phrase placement success as the object reaching and remaining at its target, not as the robot continuing to hold it.
Resolve the endpoint
Use a caller-provided OpenAI-compatible vision endpoint/model first. Local Agent exposes that configuration as I4H_AGENT_VL_BASE_URL, I4H_AGENT_VL_MODEL, and either I4H_AGENT_VL_API_KEY or I4H_AGENT_API_KEY. Map those generic agent variables to the annotator without printing the credential:
VLM_ARGS=()
if [ -n "${I4H_AGENT_VL_BASE_URL:-}" ] && [ -n "${I4H_AGENT_VL_MODEL:-}" ]; then
I4H_VLM_URL="${I4H_AGENT_VL_BASE_URL%/}"
case "$I4H_VLM_URL" in */v1) ;; *) I4H_VLM_URL="$I4H_VLM_URL/v1" ;; esac
export I4H_VLM_URL
export OPENAI_API_KEY="${I4H_AGENT_VL_API_KEY:-${I4H_AGENT_API_KEY:-EMPTY}}"
VLM_ARGS=(--model "$I4H_AGENT_VL_MODEL")
fiIf no caller-provided endpoint/model is available, start the repository's local service:
tools/annotator/scripts/vllm.sh ensureRecord whether this invocation started it. Do not hard-code a model name in the skill; use the CLI/service defaults unless the user supplies one.
Dry-run sampling when needed
uv run --project tools/annotator i4h-annotator \
--task "<success criterion>" \
--dry-run \
offline /absolute/path/to/recording.hdf5Use this to verify cameras and sampled frames without transmitting images.
Grade and filter
RUN_DIR="$(pwd)/runs/<workflow>/$(date +%Y%m%d_%H%M%S)"
mkdir -p "$RUN_DIR"
uv run --project tools/annotator i4h-annotator \
--task "<success criterion>" \
"${VLM_ARGS[@]}" \
offline /absolute/path/to/recording.hdf5 \
--writeAdd global --base-url, --model, --camera, or --frames only when selected. Add offline --node only for a requested segment. Add --filter "$RUN_DIR/filtered.hdf5" only when filtering was requested; a summarize-only prompt must grade all episodes without requiring at least one success. Keep credentials in environment variables; never print them.
Stop the local VLM only if this invocation started it:
tools/annotator/scripts/vllm.sh stopVerify
Inspect the annotator summary. If filtering was requested, also inspect the filtered file:
uv run --project tools/dataset i4h-dataset inspect "$RUN_DIR/filtered.hdf5" --segmentsRequire a verdict for every selected episode and reconcile pass/fail counts plus filtered counts when applicable. Treat endpoint errors, absent cameras, partial writes, and unexplained zero-episode output as failure. An all-failure verdict set is a valid completed grading run for summarize-only prompts; it is not a valid filtered dataset.
Troubleshooting
Check camera sampling before endpoint/authentication errors. Never accept partial writes or a filtered file with an unexplained zero count.
Prerequisites
Require a readable workflow HDF5 with camera frames and, unless dry-running, a reachable OpenAI-compatible vision endpoint.
Limitations
Visual grading cannot recover missing frames or prove simulator state that is not visible.
Examples
Run annotation on all recorded episodes and summarize.→ select the current HDF5, grade every episode, verify the filtered file, and report pass/fail counts.
Completion gate
Report source HDF5, selected criterion/camera/model/endpoint origin, graded pass/fail counts, filtered path/count when requested, dry-run result if used, and local-service cleanup.
Files
- ATTRIBUTION.mdAdded by AutoGineer964 B
- BENCHMARK.md6.8 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md5.8 KiB
- evals/evals.json1.3 KiB
- skill-card.md3.9 KiB
- skill.oms.sig4.5 KiB
LICENSE
Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright [yyyy] [name of copyright owner]
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
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 an API key, 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-annotate · 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-annotate.zip