physical-ai-video-data-augmentation — Claude Code skill: VDA on OSMO
Runs the video augmentation and auto-labeling loop on OSMO, from picking a flow and preflight checks to monitoring and pulling outputs.
npx skills add NVIDIA/skills --skill physical-ai-video-data-augmentation -a claude-codeWhat physical-ai-video-data-augmentation does
This skill handles OSMO VDA runs so you do not have to chain the steps by hand. It maps your request to one of four flows: auto_labeling, augmentation_and_al, e2e, e2e_super_resolution. Then the agent walks the run from preflight to downloaded results.
Several scripts do the work. Credentials and image access are checked by preflight_credentials.sh. Interpolation values, cache state and dataset safety pass through pre_submit_guard.py. Demo clips come from prepare_demo_assets.sh. Separate workers cover augmentation and labeling, a barrier script keeps multiple nodes in step, and a renderer builds a side-by-side view of augmented flows.
Three things must be in place. The osmo CLI has to be on PATH, logged in, with a DATA credential profile. A Hugging Face token is needed for the gated Cosmos and SeedVR weights. And osmo pool list --mode free must show at least one ONLINE GPU pool, otherwise nothing gets scheduled. An NGC API key is optional. The skill also tells the agent to read storage_url from the active dataset backend instead of guessing it; a wrong scheme breaks storage auth at runtime.
What it helps with
- match a VDA request to the right flow
- verify secrets and image access before submitting
- take
storage_urlfrom the dataset backend, not from guesswork - follow a workflow through to finished outputs
- render a before-and-after view of augmented video
Example prompts for the agent
- Label my source videos only, use the
auto_labelingflow - Start the full VDA pipeline on OSMO and list the GPU pools you can target
- Augment the videos, auto-label the outputs, then render a side-by-side comparison
How to install physical-ai-video-data-augmentation
1Skills CLI (Vercel)
npx skills add NVIDIA/skills --skill physical-ai-video-data-augmentation -a claude-code2Manually: put the files in place
~/.claude/skills/physical-ai-video-data-augmentation/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/physical-ai-video-data-augmentation/
1Skills CLI (Vercel)
npx skills add NVIDIA/skills --skill physical-ai-video-data-augmentation -a codex2Manually: put the files in place
.agents/skills/physical-ai-video-data-augmentation/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add NVIDIA/skills --skill physical-ai-video-data-augmentation -a cursor2Manually: put the files in place
.cursor/skills/physical-ai-video-data-augmentation/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install NVIDIA/skills physical-ai-video-data-augmentation2Manually: put the files in place
.github/skills/physical-ai-video-data-augmentation/SKILL.md1Skills CLI (Vercel)
npx skills add NVIDIA/skills --skill physical-ai-video-data-augmentation -a gemini-cli2Platform CLI
gemini skills install https://github.com/NVIDIA/skills --path skills/physical-ai-video-data-augmentationManually: put the files in place
.devin/skills/physical-ai-video-data-augmentation/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add NVIDIA/skills --skill physical-ai-video-data-augmentation -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about physical-ai-video-data-augmentation
How do I install physical-ai-video-data-augmentation in Claude Code?
Run npx skills add NVIDIA/skills --skill physical-ai-video-data-augmentation -a claude-code. The skill lands in ~/.claude/skills/physical-ai-video-data-augmentation/SKILL.md.
What does physical-ai-video-data-augmentation do?
It orchestrates a VDA workflow on OSMO: picks the flow, runs preflight, submits with explicit interpolation values, monitors the run and retrieves outputs.
What does this skill need to run?
The osmo CLI on PATH with a login and DATA credential profile, a Hugging Face token (via HF_TOKEN or the local cache), and at least one ONLINE GPU pool. Without a pool the job has nowhere to go.
