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name: pipelines-and-nodes description: "Design, validate, refactor, namespace, tag, slice, inspect, and troubleshoot Kedro Node and Pipeline objects." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Kedro Pipelines and Nodes

Use this sub-skill when a task is about Kedro graph authoring: node(), Node, Pipeline, pipeline(), namespaces, tags, slicing, graph inspection, modular pipeline functions, GroupedNodes, preview payloads, or LLM context nodes.

Stay inside pure graph semantics here. For execution with kedro run or runners, read ../runners-and-execution/SKILL.md; for DataCatalog, dataset YAML, credentials, or parameters files, read ../data-catalog-and-config/SKILL.md; for project scaffolding and kedro pipeline create/delete, read ../project-cli-and-sessions/SKILL.md.

Start Here

1. Import current Kedro pipeline APIs from kedro.pipeline: node, Node, Pipeline, pipeline, GroupedNodes, and optional experimental preview/LLM helpers.
2. Prefer pipeline(...) or Pipeline(...) for reusable and namespaced pipelines. Do not import kedro.pipeline.modular_pipeline; it is absent in Kedro 1.4.0.
3. Keep node functions pure and make every dataset edge explicit through inputs, outputs, and params: names.
4. Use pipeline(base_pipeline, inputs=..., outputs=..., parameters=..., namespace=...) to reuse graph templates without duplicate node or dataset collisions.
5. Use Pipeline.describe(), pipeline.inputs(), pipeline.outputs(), pipeline.datasets(), pipeline.nodes, pipeline.grouped_nodes, pipeline.group_nodes_by(), and pipeline.to_json() before changing or executing a graph.

References

• Read references/api-reference.md for current signatures, allowed node input/output shapes, graph methods, preview contracts, LLM context nodes, and registry-facing APIs.
• Read references/workflows.md for reusable namespaced pipeline templates, modular pipeline structure, graph slicing, registry interactions, and inspection patterns.
• Read references/troubleshooting.md when Kedro raises invalid node, duplicate output, circular dependency, namespacing, parameter remapping, preview, or stale-import errors.
• Run scripts/smoke_pipeline.py --help to inspect the bundled smoke check, then scripts/smoke_pipeline.py to build and run a tiny in-memory pipeline when core Kedro execution dependencies are installed.

Decision Rules

• Use node(func, inputs, outputs, name=..., tags=...) for one callable step; use Node(...) only when matching class-based code style.
• Use string inputs/outputs for one dataset, lists for positional arguments or multiple outputs, and dicts when function argument names or returned dictionary keys differ from Kedro dataset names.
• Use params:name for single parameters and parameters only when the node function should receive the whole parameters dictionary.
• Use pipeline-level parameters=... for reusable parameter remapping; never put params:* or parameters under reusable inputs=....
• Use pipeline-level namespace=... to make reused pipelines unique. Explicit inputs, outputs, and parameters mappings expose connection points that should not receive the namespace prefix.
• Use prefix_datasets_with_namespace=False only when the namespace is a grouping label for nodes and dataset names must remain unchanged.
• Use node-level namespace=... mainly for visual grouping; it prefixes node names but does not behave like pipeline-level dataset remapping.
• Use pipeline.filter(...) when several slice conditions must all apply; use only_nodes_with_tags() for OR-style tag selection.

Common Cross-Routes

• If a graph is valid but inputs are missing from the catalog, switch to ../data-catalog-and-config/SKILL.md for catalog entries or to ../runners-and-execution/SKILL.md for run selection.
• If a task asks for kedro run --from-nodes, --to-outputs, --tags, --namespaces, or --only-missing-outputs, use this sub-skill to validate slice semantics, then route command construction to ../runners-and-execution/SKILL.md.
• If a task asks where to place create_pipeline() or how find_pipelines() discovers it, read this sub-skill for API requirements and ../project-cli-and-sessions/SKILL.md for project layout and CLI behavior.
• If LLM context nodes touch real LLM clients, prompt datasets, credentials, or serving endpoints, keep graph declaration here and route dataset/config/runtime concerns to sibling sub-skills.