---

name: pipeline-authoring description: "Build, configure, run, schedule, debug, and validate ZenML step and pipeline workflows, artifacts, materializers, settings, hooks, wait/resume, and local execution patterns." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Pipeline Authoring

Use this sub-skill when the task is to write or debug ZenML user code built with @step, @pipeline, artifacts, materializers, pipeline settings, schedules, caching, retries, hooks, dynamic pipelines, or local execution.

Natural Triggers

• User asks for a ZenML pipeline, workflow, DAG, step, artifact, materializer, cache, retry, hook, schedule, dynamic pipeline, wait/resume flow, or local run.
• User has Python functions and wants them wrapped in @step or composed with @pipeline.
• User needs typed multi-output artifacts, custom output materializers, DockerSettings, ResourceSettings, or YAML/runtime configuration.
• User is debugging cache misses, skipped hooks, invalid materializer output keys, dynamic .load()/.chunk()/.map() behavior, or duplicate child pipeline runs.

Route First

1. Read API reference for decorator signatures, settings keys, output naming, materializer contracts, schedules, hooks, retries, and dynamic APIs.
2. Read Workflow recipes for concrete static, dynamic, scheduled, materialized, hook-enabled, and isolated-local patterns.
3. Read Troubleshooting before changing cache/retry/hook behavior, dynamic child pipeline structure, Docker/resource settings, or local ZenML configuration.
4. Run inspect_pipeline_api.py when the installed ZenML version may differ from this skill, or before relying on a signature.
5. Run pipeline_smoke.py with --check-imports for a safe import probe, or with --run for an opt-in temporary local pipeline smoke.

Boundary Rules

• Own user-facing pipeline code: decorators, step signatures, dataflow, artifact typing, materializer selection, settings dictionaries, caching, retries, hooks, dynamic execution, wait/resume, schedules, and local smoke checks.
• Route stack component registration, orchestrator implementation, step operator implementation, integration extras, and component flavor code to stacks-and-integrations.
• Route CLI/client resource listing, schedule lifecycle commands, run audit scripts, and remote-server queries to cli-and-client.
• Route FastAPI server, store, trigger model, migration, and RBAC internals to server-and-stores.
• Route deployment services, model/agent deployment examples, production serving, and credential-heavy operational examples to deployments-and-agents.
• Route repository test selection, formatting, linting, docs maintenance, and CI-equivalent checks to maintenance.

Working Pattern

• Start with ordinary typed Python functions, make them importable at module level, then add @step and @pipeline only where orchestration is needed.
• Prefer with_options(...) for per-run or per-invocation changes; reserve configure(...) for deliberate in-place global mutation of a step or pipeline object.
• Name multiple outputs with typing.Annotated before assigning per-output materializers or downstream references.
• Keep parameters JSON-serializable; pass complex datasets, models, directories, and custom objects as artifacts with explicit type annotations or materializers.
• For dynamic pipelines, use .load() only for Python control flow decisions and .chunk(index=...), .map(...), .product(...), unmapped(...), or futures to wire DAG dependencies.
• Treat cache, retry, and hook behavior together: cache hits skip step execution and step hooks, retries fire attempt hooks repeatedly, and hook failures are recorded without aborting the run.

Safe Validation

• python scripts/inspect_pipeline_api.py --check-imports verifies that the expected public APIs import from the active environment.
• python scripts/inspect_pipeline_api.py --json prints decorator/settings/materializer signatures for the active ZenML installation.
• python scripts/pipeline_smoke.py --check-imports checks importability without creating a ZenML repository or run.
• python scripts/pipeline_smoke.py --run creates a temporary isolated ZenML config and local repository, runs a tiny local pipeline, and cleans up by default.