zenml-io-skills

Plugin zenml-io-skills from marketplace cascade-ai-ml-engineering in joshuarweaver/cascade-ai-ml-engineering. Commands read from the repo's manifest — 1 month ago.

Skills (14)

• airflow-to-zenml-migration

  Migrate Apache Airflow DAGs, operators, and workflows to idiomatic ZenML pipelines. Handles concept mapping (DAG→pipeline, operator→step, XCom→artifact), code translation, scheduling, retry config, Docker settings, and flags unsupported patterns (trigger rules, sensors, dynamic task mapping) for hum

• argo-to-zenml-migration

  Migrate Argo Workflows, WorkflowTemplates, ClusterWorkflowTemplates, and CronWorkflows to idiomatic ZenML pipelines. Handles concept mapping, YAML-to-Python translation, scheduling, retries, Kubernetes-native pattern analysis, and flags unsupported patterns such as status-based depends logic, shared

• azureml-to-zenml-migration

  Migrate Azure Machine Learning SDK v2 pipelines, components, environments, and schedules to idiomatic ZenML pipelines. Handles concept mapping (`@pipeline` -> `@pipeline`, `@command_component` -> `@step`, `Environment(...)` -> `DockerSettings(...)`, AzureML compute -> `AzureMLOrchestratorSettings`),

• dagster-to-zenml-migration

  Migrate Dagster assets, ops, graphs, jobs, and software-defined asset workflows to idiomatic ZenML pipelines. Handles concept mapping (asset->step output, job->pipeline, IOManager->artifact store/materializer + explicit IO steps), asset-boundary planning, code translation, scheduling, retry config,

• databricks-to-zenml-migration

  Migrate Databricks Workflows (Lakeflow Jobs) to idiomatic ZenML pipelines. Handles concept mapping (Job->pipeline, Task->step, task values->artifact), notebook refactoring, code translation for all Databricks task types (notebook_task, python_wheel_task, sql_task, dbt_task, condition_task, for_each_

• flyte-to-zenml-migration

  Migrate Flyte workflows, tasks, LaunchPlans, and Flytekit code to idiomatic ZenML pipelines. Handles concept mapping (`@task`->`@step`, `@workflow`->`@pipeline`, `map_task()`->dynamic `.map()`, `conditional()`->dynamic branching, `LaunchPlan`->schedule/config split), code translation, special-type m

• kedro-to-zenml-migration

  Migrate Kedro pipelines and projects to idiomatic ZenML pipelines. Handles concept mapping (node->step, Pipeline->pipeline, Data Catalog->explicit boundary steps plus artifacts, params:->typed parameters), catalog analysis, code translation, hooks/runners/deployment mapping, and flags unsupported pa

• metaflow-to-zenml-migration

  Migrate Metaflow flows and Outerbounds-flavored Metaflow projects to idiomatic ZenML pipelines. Handles concept mapping (FlowSpec->pipeline, @step->@step, self.* artifacts->explicit returns and inputs), code translation for Parameters, IncludeFile, Config, self.next transitions, branch/join, foreach

• zenml-pipeline-authoring

  Author ZenML pipelines: @step/@pipeline decorators, type hints, multi-output steps, dynamic vs static pipelines, artifact data flow, ExternalArtifact, YAML configuration, step caching (enable_cache at pipeline, step, and YAML level), DockerSettings and ResourceSettings for remote execution, orchestr

• prefect-to-zenml-migration

  Migrate Prefect flows, tasks, and deployment patterns to idiomatic ZenML pipelines. Handles concept mapping (`@flow`→`@pipeline`, `@task`→`@step`, result persistence→artifacts), dynamic-execution analysis, code translation, scheduling, retries, Blocks/secrets decomposition, and flags unsupported pat

• zenml-quick-wins

  Implements ZenML quick wins to enhance MLOps workflows. Investigates codebase and stack configuration, recommends high-priority improvements, and implements metadata logging, experiment tracking, alerts, scheduling, secrets management, tags, git hooks, HTML reports, Model Control Plane setup, live s

• sagemaker-to-zenml-migration

  Migrate Amazon SageMaker Pipelines and workflow code to idiomatic ZenML pipelines. Handles concept mapping (Pipeline->@pipeline, ProcessingStep/TrainingStep->@step, PropertyFile/JsonGet->artifacts), code translation, SagemakerOrchestratorSettings mapping, scheduling, model-registration strategy, and

• zenml-scoping

  Scope and decompose ML workflow ideas into realistic ZenML pipeline architectures. Runs an in-depth interview to help users break down ambitious or over-engineered plans into well-composed multi-pipeline setups, identify what belongs in a pipeline vs. what doesn't, define cross-pipeline data flow vi

• vertexai-to-zenml-migration

  Migrate Vertex AI Pipelines (Kubeflow Pipelines v2 / PipelineJob workflows) to idiomatic ZenML pipelines. Handles concept mapping (`@dsl.pipeline` -> `@pipeline`, `@dsl.component` -> `@step`, `PipelineJob.create_schedule(...)` -> `Schedule(...)`), artifact-contract translation (`Input[Dataset]`, `In

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