rhecosystemappeng/openshift-ai-skills
Add and configure Red Hat OpenShift AI components in your project. Interactive skills for model serving, pipelines, model registry, RAG, training, and more.
Add a Red Hat OpenShift AI component to an existing project, wire it into the application, and verify it on every deployment target. Use when the user wants to add, deploy, or integrate a specific OpenShift AI component such as KServe, vLLM, Model Registry, Data Science Pipelines, or any component from the catalog.
Evaluate whether migrating existing code or infrastructure to a specific OpenShift AI component is feasible. Use when the advisor needs a migration difficulty verdict for a single component.
Analyze a project and recommend OpenShift AI components. Use when the user wants guidance on OpenShift AI architecture, asks "what should I deploy", or wants to integrate OpenShift AI into an existing project.
Scan a project for ML/AI patterns, model artifacts, frameworks, and infrastructure signals. Use when the advisor needs a structured inventory of what ML/AI work a project already does.
Write black-box tests for an OpenShift AI component using only its public contract. Use when the main agent has finished implementing a component and needs unbiased test coverage.