---

name: kubeflow-pipelines description: "Route Kubeflow Pipelines SDK, client, Kubernetes-addon, and repository-maintenance workflows to the right sub-skill." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Kubeflow Pipelines

Use this repo skill for Kubeflow Pipelines (KFP) tasks that involve the Python SDK, client/registry flows, Kubernetes task configuration, or this checkout's maintainer workflow. The runtime skill is split into sub-skills so future agents can route quickly without reading the whole repository.

Start here

If the request is about using KFP as a library, the bundled public-use sub-skills own the task:

• sub-skills/pipeline-authoring/ for DSL components, pipelines, artifacts, task modifiers, control flow, and local execution smoke checks.
• sub-skills/compiler-and-cli/ for Compiler().compile, kfp dsl compile, dsl-compile, and compile-output questions.
• sub-skills/client-and-registry/ for kfp.Client, client-backed CLI groups, uploads, runs, recurring runs, experiments, and registry package flows.
• sub-skills/kubernetes-platform/ for kfp.kubernetes helper APIs that add Kubernetes-specific task configuration.

If the request is about changing this checkout, generated files, tests, CI, manifests, frontend/backend code, or developer setup, route to sub-skills/repo-development/.

Package map

Read references/package-overview.md before answering install, import, version, or package-layout questions. It summarizes the kfp namespace package, the kfp-pipeline-spec and kfp-kubernetes add-ons, the main console scripts, and the current repo layout at a high level.

Read references/repo-provenance.md when you need to compare the generated skill against the source checkout state, and references/repo-routing-metadata.json when you need the router metadata that places this skill inside the repo-skills router.

Environment check

Use scripts/check_kfp_environment.py as a safe first smoke test when you need to confirm that the installed KFP environment is usable. It checks the public import surface and can optionally probe CLI/help behavior without relying on the original checkout.

Troubleshooting

Read references/troubleshooting.md when the user reports import failures, endpoint confusion, local-runner issues, compile/manifest confusion, version skew, or checkout-maintenance prerequisites.

Routing hints

• If the user wants to write a pipeline/component, start with pipeline-authoring.
• If the user already has a Python file or object and wants YAML or manifest output, use compiler-and-cli.
• If the user wants to submit or manage runs, experiments, or registry packages, use client-and-registry.
• If the user wants Kubernetes-specific task settings, use kubernetes-platform.
• If the user is editing source files or regen outputs in this repo, use repo-development.

Notes for future agents

• The public-use sub-skills are self-contained and should not depend on this construction checkout.
• Do not treat skills/tests/ as runtime content; it is the review and verification area.
• Use the smallest sub-skill that actually matches the task, then read only the bundled references and scripts that that sub-skill points to.