Agent skills and workflow documentation for generating labeled synthetic training data for physical-AI inspection and perception models — without waiting for every real defect or rare event to appear in production.
This repository does not ship application code. It ships agent skills: prompt-driven orchestrators that an AI agent (Claude Code, Codex, OpenClaw/Claw, or any compatible agent) follows to run end-to-end synthetic data generation pipelines on NVIDIA OSMO from a provisioned GPU runtime environment.
| Skill | What it does | Use cases |
|---|---|---|
| Defect Image Generation (DIG) | Synthesizes labeled defect and clean images for Automated Optical Inspection (AOI) by chaining USD rendering (USD-to-ROI), Qwen Image-Edit appearance transfer, and Cosmos AnomalyGen — with optional finetuning. Day 0 (cold start from CAD/USD) and Day 1 (inference + labeling on real photos) paths. | PCBA, metal surface, glass |
| Video Data Augmentation (VDA) | Runs video augmentation and auto-labeling (pseudo-labeling) on OSMO using Cosmos, covering flow selection, preflight, submit-time interpolation, monitoring, and output retrieval. | Video analytics / event video |
Each skill is self-contained under skills/<name>/, with its canonical OSMO
workflow YAMLs in assets/configs/, use-case cookbooks in assets/cookbooks/,
and long-form guidance in references/.
skills/ Agent skills (the product). Canonical source.
docs/ Per-workflow environment guides and setup walkthroughs.
.claude/ .codex/ .agents/ Agent-tool entry points (symlinked to skills/).
The .claude/skills, .codex/skills, and .agents/skills directories are
symlinks to skills/, so the same canonical skill is discovered automatically
by each agent runtime.
The fastest path is to provision a GPU runtime environment, pull sample assets, and open an agent (Claw or your preferred coding agent) that drives the workflow for you:
- Defect Image Generation — see docs/workflows/physical-ai-defect-image-generation/launchable.md
- Video Data Augmentation — see docs/workflows/physical-ai-video-data-augmentation/launchable.md
To use a skill directly with your own agent, point the agent at the relevant
skills/<name>/SKILL.md and follow its prompts. The skill handles flow
selection, preconditions, data handoffs, and OSMO submit/monitor commands.
Workflows run on OSMO and pull gated assets, so you'll generally need:
- NGC credentials to pull container images from
nvcr.io. - A Hugging Face read token for gated Cosmos / AnomalyGen / Qwen-Image-Edit models and datasets (accept each model's license first).
- OSMO CLI access with a logged-in profile and an available GPU pool.
The exact secrets, model/dataset links, and resource sizing are listed in each workflow's environment guide.
This project is currently not accepting external contributions. The repository is published as-is for reference and reproducibility; issues and pull requests from community members will not be reviewed or merged at this time. If contributions reopen, all commits must be signed off per the Developer Certificate of Origin — see CONTRIBUTING.md.
To report a vulnerability, contact the NVIDIA PSIRT rather than opening a public issue. See SECURITY.md.
Code is licensed under Apache-2.0 and documentation/skill content under
CC-BY-4.0 (CC-BY-4.0 AND Apache-2.0). See LICENSE.