---

name: kubernetes-platform description: "Attach Kubernetes-specific kfp-kubernetes task configuration to Kubeflow Pipelines PipelineTask objects and verify the compiled platform YAML without requiring a cluster." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Kubernetes Platform

Use this sub-skill when the user already has a Kubeflow Pipelines DSL task and needs kfp-kubernetes platform configuration: Secrets, ConfigMaps, PVCs, ephemeral volumes, node placement, tolerations, pod metadata, image pull settings, field-path env vars, security context, timeout, or init containers.

Route first

Stay in this sub-skill for:

• installing/importing the kfp-kubernetes addon and checking its public kfp.kubernetes helper surface;
• applying Kubernetes helpers to dsl.PipelineTask objects returned by component calls;
• compile-only verification that the generated YAML contains Kubernetes platform markers.

Route away when the request is really about:

• general DSL components, pipeline structure, type hints, task dependencies, or artifacts -> pipeline-authoring;
• compiler flags, CLI wrapper details, package paths, or manifest-format options -> compiler-and-cli;
• submitting/running against a live KFP endpoint or proving runtime Secret/PVC/ConfigMap existence -> client-and-registry or repo-development depending on context;
• repository maintainer internals, backend manifests, deployment configuration, or generated Kubernetes resources -> repo-development.

Operating checklist

1. Verify imports and package alignment before advising on helper behavior:

   import importlib.metadata as metadata
   import kfp
   from kfp import kubernetes
   
   assert metadata.version("kfp-kubernetes") == kfp.__version__
   print(kubernetes.__all__)
   

   Install with pip install kfp[kubernetes] or pip install kfp-kubernetes when the addon is absent.
2. Attach helpers only to a PipelineTask, for example task = my_component(). Do not pass the component function, a ContainerSpec, or the pipeline function itself.
3. Prefer public helpers exported from kfp.kubernetes.__all__; see references/api-reference.md for signatures, return behavior, and YAML marker names.
4. Compile the pipeline and inspect the platform document/section. Kubernetes helper calls serialize under platforms.kubernetes.deploymentSpec.executors.<executor>, not in ordinary component code.
5. State the verification boundary: compile success proves that the DSL emitted platform configuration, but it does not create cluster resources or prove that a Kubernetes Secret, ConfigMap, PVC, StorageClass, node label, toleration target, or admission policy exists.

Fast compile smoke

Run the bundled smoke helper in an environment with matching kfp and kfp-kubernetes installed. Resolve the script path relative to this sub-skill directory:

python scripts/compile_kubernetes_config_pipeline.py \
  --output /tmp/kubernetes-platform-smoke.yaml

The script compiles a tiny pipeline, asserts that helper calls return the same PipelineTask, and checks for representative secretAsEnv, nodeSelector, and tolerations markers in the YAML. It never contacts a Kubernetes cluster.

Reference map

• references/api-reference.md: installation, public helpers, signatures, return behavior, and platform YAML markers.
• references/workflows.md: practical compile-only workflows for Secrets/ConfigMaps, PVCs/volumes, scheduling, pod configuration, and validation boundaries.
• references/troubleshooting.md: import/version failures, wrong-object usage, compile-vs-runtime confusion, and invalid Kubernetes inputs.

Native evidence anchors

This skill is grounded in the kfp-kubernetes README/setup metadata, the exported kfp.kubernetes source modules, Sphinx API stubs, installed package facts, unit tests for Secret/ConfigMap/volume helpers, snapshot compile/read-write coverage, the core PVC sample, and SDK compilation tests that compare generated platform specs.