Individual skill
cdf-defect-image-generation
Generate synthetic PCB inspection images on Kubernetes with the SMT Defect Image Generation (DIG) DAG in CDF Orchestration. The DAG renders components in simulation, turns the renders into realistic golden images (sim2real), and uses AnomalyGen to add texture defects such as solder bridges, excess solder or missing components. Simulation can also render shift, tombstone and sideflip pose defects. Use it to build DIG payloads, pick a pipeline mode (full, simulation, sim2real or defect-only), check GPU readiness, follow a run, and match each defect image to its golden and simulated source. Not for running Simulation, Augmentation or AnomalyGen on their own.
skills/cdf-defect-image-generation/SKILL.md
Source commit fce3cca4170be75e82c64920654d56ee2811b005
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Network or broad shell
- Last checked
- Nov 9, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 3,928
- Repository last updated
- Nov 11, 2026
- License
- Apache-2.0
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agent-skills Repository topic
- agentic-ai Repository topic
- ai-agents Repository topic
- claude-code Repository topic
- codex Repository topic
- coding-agents Repository topic
- cuda Repository topic
- developer-tools Repository topic
- llm Repository topic
- omniverse Repository topic
- physical-ai Declared skill tag
- cdf-orchestration Declared skill tag
- defect-image-generation Declared skill tag
- smt Declared skill tag
- devops Derived category
- devops-containers Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
BENCHMARK.mddoc · 10,795 bytesSKILL.mdcore · 18,362 bytesSKILL.mdcore · 18,362 bytesSKILL.mdcore · 18,362 bytesSKILL.mdcore · 18,362 bytesagents/openai.yamlasset · 369 bytesevals/EVAL.mddoc · 4,682 bytesevals/evals.jsonasset · 20,656 bytesevals/test_payload.pyruns · 10,639 bytesreferences/airflow-direct-api.mddoc · 6,408 bytesreferences/outputs.mddoc · 3,354 bytesreferences/payload-contract.mddoc · 17,799 bytesreferences/setup-and-preflight.mddoc · 5,434 bytesreferences/troubleshooting.mddoc · 4,485 bytesscripts/payload.pyruns · 10,702 bytesskill-card.mddoc · 6,512 bytesskill.oms.sigasset · 6,725 bytes
Read excerpt
# CDF Orchestration — SMT Defect Image Generation
Run `defect_image_generation_smt_dag_k8s`: CDF Simulation renders PCB components,
CDF Augmentation turns the renders into realistic clean (golden) images, and
CDF AnomalyGen adds texture defects. Simulation can also render pose defects
(shift, tombstone, sideflip). One DAG serves every mode; `pipeline_mode` selects
which stages run.
## Requirements
Execution needs an authorized Kubernetes connection, a reachable Airflow 3
controller with the DIG DAG loaded, and storage and model access for the chosen
mode. The payload checker needs a trusted CDF Orchestration checkout and its
Airflow Python environment. Explaining the workflow or drafting JSON needs neither.