Migrate Argo Workflows to ZenML

This skill translates Argo Workflows into idiomatic ZenML pipelines. It handles the full migration workflow: analyzing Argo YAML and referenced scripts, classifying each pattern, translating what maps cleanly, flagging what needs redesign, and producing a working ZenML project.

How migration works at a high level

Argo and ZenML can both express DAG-shaped workflows, but they speak different native languages.

• Argo is a Kubernetes-native workflow engine driven by YAML CRDs, template references, variable substitution, pod topology, and controller policies.
• ZenML is a Python-first pipeline framework driven by @step, @pipeline, typed artifacts, stack components, and orchestrator-managed execution.

That means this migration is not a simple rename operation. Some Argo features map well, some only preserve intent, and some depend so heavily on controller or pod semantics that they must be redesigned explicitly.

The three mapping types

Every Argo concept falls into one of these categories: