Author ZenML Pipelines

This skill guides pipeline authoring: steps, artifacts, configuration, Docker settings, materializers, metadata, secrets, and visualizations.

Start Here: Interview the User

Do not rush to code. Before writing a single line, thoroughly understand what the user wants to build. The interview is the most important step — a well-scoped pipeline that does 3 things well beats a sprawling one that does 10 things poorly.

For complex or multi-pipeline projects: If the user describes something ambitious (e.g., "build me an end-to-end ML platform with data ingestion, feature engineering, training, evaluation, deployment, monitoring, and retraining"), or if they mention multiple pipelines, invoke the zenml-scoping skill first. It runs a deeper architectural interview that decomposes the system into pipeline units, identifies what doesn't belong in a pipeline at all, and produces a pipeline_architecture.md spec. Once that's done, come back here to build each pipeline one at a time.

For single, focused pipelines: If the user's request is clearly one pipeline (e.g., "build a training pipeline for my CSV data"), proceed with the questions below. If the answers are obvious from context, infer them and proceed. Only ask when genuinely ambiguous.

Q1: Static or dynamic pipeline? Most pipelines are static (fixed DAG). Use dynamic (@pipeline(dynamic=True)) only when the number of steps or their wiring depends on runtime values (e.g., "process N documents where N comes from a query"). See Dynamic Pipelines and references/dynamic-pipelines.md.

Q2: Local or remote orchestrator? If remote (Kubernetes, Vertex AI, SageMaker, AzureML), the Artifact Golden Rule is critical, and you will need Docker Settings. If local-only for now, you can defer those concerns. Ask whether the user already has a stack set up — if not, point them to the ZenML docs for stack setup (this skill does not cover stack creation).