physical-ai-defect-image-generation
physical-ai-defect-image-generation is an agent skill published by nvidia, listed under deploy & operate. It lives in the NVIDIA/skills repository, which has 3,735 GitHub stars and is licensed under Apache-2.0; we last read it on 2026-09-16.
Use when the user wants to orchestrate defect image generation with NVIDIA Cosmos AnomalyGen (Cosmos-Predict2-derived) on OSMO for PCBA, metal surface, and glass inspection. The Day 0 path handles cold-start with USD-to-ROI, image-edit augmentation, and AnomalyGen to create initial PCBA datasets. The Day 1 path performs inference and labeling on real images. This skill helps with first-time asset setup, creation of finetuning checkpoints, and configuring deployment. Trigger keywords: defect image generation, dig workflow, dig pipeline, defect image detection workflow, aoi pipeline, aoi anomalygen, usd2roi anomalygen, day 0 pcba, day 1 pcba, day 1 real-photo alignment, day 1 manual roi, metal surface anomaly, glass defect, anomalygen finetune, setup_pcb, setup_metal, setup_glass, setup_pretrained, dig setup, dig datasets, dig pretrained checkpoint, dig image-edit endpoint, cosmos defect generation, cosmos-predict2 defect, cosmos-anomalygen, cosmos predict2 finetune.
Install
skills is a generic installer for any repository of Claude agent skills -- it reads NVIDIA/skills’s own structure rather than assuming one, the same way this page reads the skill’s own SKILL.md rather than assuming a format.
npx skills add NVIDIA/skills
What it connects to
Unverified means a mention in the skill’s own description, not a citation. A wrong claim here costs nothing to correct and nothing to trust yet.
Maintenance
Use when the user wants to orchestrate defect image generation with NVIDIA Cosmos AnomalyGen (Cosmos-Predict2-derived) on OSMO for PCBA, metal surface, and glass inspection. The Day 0 path handles cold-start with USD-to-ROI, image-edit augmentation, and AnomalyGen to create initial PCBA datasets. The Day 1 path performs inference and labeling on real images. This skill helps with first-time asset setup, creation of finetuning checkpoints, and configuring deployment. Trigger keywords: defect image generation, dig workflow, dig pipeline, defect image detection workflow, aoi pipeline, aoi anomalygen, usd2roi anomalygen, day 0 pcba, day 1 pcba, day 1 real-photo alignment, day 1 manual roi, metal surface anomaly, glass defect, anomalygen finetune, setup_pcb, setup_metal, setup_glass, setup_pretrained, dig setup, dig datasets, dig pretrained checkpoint, dig image-edit endpoint, cosmos defect generation, cosmos-predict2 defect, cosmos-anomalygen, cosmos predict2 finetune.
physical-ai-defect-image-generation compared with similar skills
| Skill | Publisher | What it does | Repo stars | Last update |
|---|---|---|---|---|
| physical-ai-defect-image-generation (this page) | nvidia | Use when the user wants to orchestrate defect image generation with NVIDIA Cosmos AnomalyGen (Cosmos-Predict2… | 3,735 | 2026-10-26 |
| amc-setup-calibration-stack(same repository) | nvidia | Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says… | 3,735 | 2026-10-26 |
| cuopt-install(same repository) | nvidia | Install cuOpt for Python, C, or server via pip, conda, or Docker; verify the install. For building cuOpt from… | 3,735 | 2026-10-26 |
| cupynumeric-hdf5(same repository) | nvidia | Read and write large cuPyNumeric arrays to HDF5 with Legate's parallel, distributed HDF5 I/O (legate.io.hdf5:… | 3,735 | 2026-10-26 |
| data-designer(same repository) | nvidia | Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline. | 3,735 | 2026-10-26 |
| deepstream-generate-pipeline(same repository) | nvidia | Build DeepStream GStreamer pipelines interactively. Use when the user asks about pipelines for video/image in… | 3,735 | 2026-10-26 |
| deepstream-import-vision-model(same repository) | nvidia | Use this skill to bring a supported object-detection vision model from HuggingFace or NVIDIA NGC into an NVID… | 3,735 | 2026-10-26 |
Skills from the same repository first, then others for this job by repository stars. Figures are from each repository when we last read it.
Common questions about physical-ai-defect-image-generation
- What does physical-ai-defect-image-generation do?
- Use when the user wants to orchestrate defect image generation with NVIDIA Cosmos AnomalyGen (Cosmos-Predict2-derived) on OSMO for PCBA, metal surface, and glass inspection. The Day 0 path handles cold-start with USD-to-ROI, image-edit augmentation, and AnomalyGen to create initial PCBA datasets. The Day 1 path performs inference and labeling on real images. This skill helps with first-time asset setup, creation of finetuning checkpoints, and configuring deployment. Trigger keywords: defect image generation, dig workflow, dig pipeline, defect image detection workflow, aoi pipeline, aoi anomalygen, usd2roi anomalygen, day 0 pcba, day 1 pcba, day 1 real-photo alignment, day 1 manual roi, metal surface anomaly, glass defect, anomalygen finetune, setup_pcb, setup_metal, setup_glass, setup_pretrained, dig setup, dig datasets, dig pretrained checkpoint, dig image-edit endpoint, cosmos defect generation, cosmos-predict2 defect, cosmos-anomalygen, cosmos predict2 finetune.
- How do I install physical-ai-defect-image-generation?
- Run npx skills add NVIDIA/skills. The skills installer reads the repository’s own structure, so it works for any repository of agent skills.
- Who publishes physical-ai-defect-image-generation?
- nvidia.
- Is physical-ai-defect-image-generation maintained, and what licence does it use?
- Its repository has 3,735 GitHub stars and its state in our directory is listed. Its last commit we recorded was on 2026-10-26. The repository declares the Apache-2.0 licence.
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