physical-ai-video-data-augmentation agent skill by NVIDIA
Runs video data augmentation and auto-labeling workflows on OSMO, covering flow selection, preflight, submit-time interpolation, monitoring, and output retrieval.
At a glance
| Publisher | NVIDIA Official |
|---|---|
| Repository | NVIDIA/skills (396 listed skills) |
| Category | AI Agents and LLM Engineering |
| License | CC-BY-4.0 AND Apache-2.0 |
| Last commit | October 17, 2026 (repository) |
| SKILL.md | 430 lines, 2,287 words |
| Bundled files | 75 files, including references, scripts, assets or templates |
| Quality score | 83/100 · B Passes all seven graded checks |
| Security scan | Not scanned |
| README badge | Get the badges for this listing |
Before you install
Read from the skill's files at the directory's October 19, 2026 snapshot: what ships with it, what its code reads, and the terms it is published under.
- Bundled scripts: 13 script files (10 Shell, 3 Python). Your agent may run them, so read them first.
- Environment variables its scripts read:
NVIDIA_API_KEY,NGC_CLI_API_KEY,HUGGING_FACE_HUB_TOKEN. Check SKILL.md for which ones it needs. - Network: its scripts use an HTTP client or a cloud SDK, and name
huggingface.co,nvcr.ioandapi.ngc.nvidia.com. - License: CC-BY-4.0 AND Apache-2.0, an open-source or open-content license.
Install physical-ai-video-data-augmentation
The skills CLI works with Claude Code, Codex, Cursor and other agents; the other routes copy the folder by hand.
npx skills add https://github.com/NVIDIA/skills/tree/main/skills/physical-ai-video-data-augmentationtmp="$(mktemp -d)"
git clone --depth 1 https://github.com/NVIDIA/skills.git "$tmp/repo"
mkdir -p .claude/skills
cp -R "$tmp/repo/"skills/physical-ai-video-data-augmentation .claude/skills/
rm -rf "$tmp"python3 ~/.codex/skills/.system/skill-installer/scripts/install-skill-from-github.py \
--repo NVIDIA/skills \
--path skills/physical-ai-video-data-augmentationNVIDIA/skills is third-party code. Read SKILL.md and any bundled scripts before you install it.
How the author describes it
Use when running video data augmentation and auto-labeling workflows on OSMO: flow selection, preflight, submit-time interpolation, monitoring, and output retrieval. Trigger keywords: video data augmentation, data enrichment, auto labeling, VDA demo, OSMO workflow, pseudo labeling.
From the description field of SKILL.md. Agents read this line to decide when to load the skill.
Sections in SKILL.md
- Purpose
- Prerequisites
- Instructions
- Available Scripts
- Supported Flows
- Pick the right workflow for the user's request
- Disambiguation: handle vague requests before committing
- Autonomous defaults (do NOT ask)
- Triggers that should pause for disambiguation
- When NOT to disambiguate
- Step 0: Select Flow and Gather Inputs
- Input video policy (non-negotiable)
- Execution Time Overview (required before run)
- Common Preconditions (all flows)
- Submit (all flows)
- OSMO Monitoring
Files in the skill folder
BENCHMARK.mdSKILL.mdagents/openai.yamlassets/configs/osmo/augmentation_and_al.yamlassets/configs/osmo/augmentation_and_pl.yamlassets/configs/osmo/auto_labeling.yamlassets/configs/osmo/e2e.yamlassets/configs/osmo/e2e_super_resolution.yamlassets/configs/osmo/setup_model_cache.yamlassets/cookbooks/FILE_INVENTORY.mdassets/cookbooks/TUNING_GUIDE.mdassets/cookbooks/city_traffic/README.mdassets/cookbooks/city_traffic/augmentation/augmentation.yamlassets/cookbooks/city_traffic/augmentation/prompts/prompt_polishing_system_prompt.mdassets/cookbooks/city_traffic/augmentation/prompts/template_generation_system_prompt.mdassets/cookbooks/city_traffic/auto_labeling/auto_labeling_config.yamlassets/cookbooks/city_traffic/auto_labeling/prompts/event_analysis.mdassets/cookbooks/city_traffic/auto_labeling/question_bank.jsonassets/cookbooks/city_traffic/workflow_config.yamlassets/cookbooks/piazza/README.mdassets/cookbooks/piazza/augmentation/augmentation.yamlassets/cookbooks/piazza/augmentation/prompts/prompt_polishing_system_prompt.mdassets/cookbooks/piazza/augmentation/prompts/template_generation_system_prompt.mdassets/cookbooks/piazza/auto_labeling/auto_labeling_config.yaml
Showing 24 of 75 files. Browse the folder on GitHub.
Suede Skill Check: 83/100 · B
Report snapshot · · graded-v2
The rubric totals 100 points; published scores are capped at 99. Security scan: not scanned.
Source revision: 4fc9cf6cec78. Report version 7416bb7592a8. Badge URLs follow the latest listing; save the report to keep this version.
Download report · Request a fresh review
What to review next
- Boundary clause: 0 of 4 points. 4 when the description says when not to use the skill (“Not for …”, “Do not use …”, “Use … instead”).
- Example request: 0 of 3 points. 3 when the description quotes an example request (3–60 characters in quotation marks).
- Tool permissions declared: 0 of 2 points. 2 when the frontmatter declares allowed-tools.
Add declarations and bundled files when they describe the skill’s actual needs. Update the source, then request a fresh review with the changed commit or PR.
physical-ai-video-data-augmentation passes the required frontmatter check and all seven graded checks, which cover the SKILL.md format, a declared license and a commit in the last year. Those checks earn 56 of the score’s points; the rest come from description quality, instruction depth, bundled files, maintenance and curated-list citations. How the score works.
See the checks and points
| Check | Result |
|---|---|
| Frontmatter parses and declares a name and a description | Pass |
| Name uses lowercase letters, digits and hyphens, 64 characters or fewer | Pass |
| Name matches the skill's folder name | Pass |
| Description is between 50 and 1,024 characters | Pass |
| Description says when an agent should use the skill | Pass |
| SKILL.md body is 500 lines or fewer | Pass |
| A license is declared in the frontmatter or the repository | Pass |
| Repository has a commit in the last 12 months | Pass |
| Score component | Points |
|---|---|
| Structural checks | 56 of 56 |
| Description length | 5 of 5 |
| Boundary clause | 0 of 4 |
| Example request | 0 of 3 |
| Instruction depth | 7 of 7 |
| Reference files | 3 of 3 |
| Bundled scripts | 2 of 2 |
| Recent maintenance | 9 of 9 |
| Curated-list citations | 1 of 7 |
| Tool permissions declared | 0 of 2 |
| Compatibility declared | 0 of 2 |
Two badges for physical-ai-video-data-augmentation
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[](https://skills.suedeai.ai/directory/nvidia/skills/physical-ai-video-data-augmentation/)
[](https://skills.suedeai.ai/directory/nvidia/skills/physical-ai-video-data-augmentation/)<a href="https://skills.suedeai.ai/directory/nvidia/skills/physical-ai-video-data-augmentation/"><img src="https://skills.suedeai.ai/directory/nvidia/skills/physical-ai-video-data-augmentation/score.svg" alt="Suede Skill Check: 83/100" width="160" height="175"></a>
<a href="https://skills.suedeai.ai/directory/nvidia/skills/physical-ai-video-data-augmentation/"><img src="https://skills.suedeai.ai/directory/nvidia/skills/physical-ai-video-data-augmentation/badge.svg" alt="Suede Skill Check: Grade B" width="160" height="175"></a>Suede Certified on the shield refers to this published structural quality check. Security scan: not scanned. Read the methodology.
Limited-time feature offer: display either the numeric or letter badge in your repository or skill README (both also qualify), then request badge verification. Eligible skills scoring 80–99 can receive a featured placement after Suede verifies at least one valid badge linked to the skill’s listing. Suede has not announced an end date.
Where this skill is cited
NVIDIA/skills appears in 1 curated list: VoltAgent/awesome-agent-skills.