Agent skill · creative production · 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.
Why this skill is useful
Adds executable workflows for defect image generation and augmentation that are specific to NVIDIA's Cosmos AnomalyGen and not commonly found in public documentation.
What it needs
Requires nvidia, osmo installed locally. About 10k tokens when loaded. Last updated 2026-08-07. 2,816 stars on the source repository.
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