Agent skill · NVIDIA
physical-ai-defect-image-generation
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.
What it needs
About 10k tokens when loaded.
What this skill does
Physical AI Defect Image Generation Table of Contents Supported Flows Disambiguation (full table in references/disambiguation.md) Step 0: Select Flow, Cookbook, and Gather Inputs Common Preconditions (long-form in references/preconditions.md) Flow walkthroughs (one entry per flow; details in references/flows/) OSMO Monitoring Supporting files End-to-end orchestration of defect image generation, augmentation, and labeling pipelines for AOI (Automated Optical Inspection) datasets. AnomalyGen = Cosmos-Predict2-2B finetuned per use case (Cosmos-AnomalyGen-PCB-2B, -Metal-2B, -Glass-2B). Every flow has a canonical OSMO workflow YAML in assets/configs/ that chains all steps non-interactively. Use-case cookbooks in assets/cookbooks/ provide PCBA usd2roi/image-edit configs and AnomalyGen training configs for PCBA, metal surface, and glass inspection. This skill governs flow selection, data handoffs, and submit commands; component internals live in each component's SKILL.md. Supported Flows Flow Entry point OSMO YAML Steps Use cases ------ ------------- ----------- ------- ----------- Day 0 — Texture Defects CAD scene USD (pcbatarget.yaml ships in the cookbook) texturedefectgenerationday0.yaml usd2roi (scangrid + per-cell ROI crops) → image-edit augmentation (nvidia/Qwen-Image-Edit-NVPCB-OVSL2SL) → finetune-or-passthrough → infer (anomalygen labels inline, including missing-component) PCBA Day 0 — Good Image (usd2roi + Image-Edit) CAD scene USD + per-board pcbatarget.yaml / day0image.yaml / day0crop.yaml goodimagegeneration.yaml usd2roi-render (scangrid + per-cell ROI crop) → Qwen Image-Edit (OVSL2SL appearance transfer) PCBA clean-image set (ChangeNet golden halves, finetune positives, real-photo pairing) Day 0 — Structural Defects CAD scene USD + per-board pcbatarget.yaml structuraldefectgeneration.yaml isaac-render (pose defects: shift / tombstone / sideflip) + per-component crop (single pod) → Qwen Image-Edit (OVSL2SL lighting transfer; pose geometry preserved) PCBA pose- …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills NVIDIA/physical-ai-defect-image-generation