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pipeline-components

pipeline-components

Assemble and debug spaCy pipeline components, factories, registry wiring, and pipe analysis.

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Skill documentation

GitHub

name: pipeline-components description: "Assemble and debug spaCy pipeline components, factories, registry wiring, and pipe analysis." disable-model-invocation: true metadata: disco-role: operating license: MIT

pipeline-components

Validated against spaCy 3.8.15 on the installed CPU environment. Optional CUDA, Apple, transformer, and language-extra behavior was not verified here.

Use this sub-skill when the task is about Language.component, Language.factory, nlp.add_pipe, component order, registry lookup, source-vs-factory config wiring, or nlp.analyze_pipes.

Read first

What this sub-skill covers

  • Registering stateless components with Language.component.
  • Registering stateful factories with Language.factory and default configs.
  • Adding components by name, reordering them, and loading components from source pipelines.
  • Reading and interpreting built-in factory metadata.
  • Using nlp.analyze_pipes() to understand assigns/requires/retokenizes relationships.

What to route elsewhere

  • Tokenization, Doc/Token/Span, matchers/rulers, serialization, and displaCy: documents-and-visualization.
  • init config, debug config, train, evaluate, convert, package, validate: training-and-cli.
  • Install/import or backend concerns: install-and-inspect.
  • spacy project orchestration: project-workflows.

Fast use pattern

  1. Check the exact component signature in the reference.
  2. Decide whether the user needs a stateless component or a stateful factory.
  3. Use the bundled registry inspection script to confirm the named component exists.
  4. Read pipeline-assembly-and-analysis.md when order, disable/enable, or source-vs-factory confusion appears.

Evidence basis

This sub-skill is grounded in the installed Language signatures, built-in factory source, pipeline tests, and public docs for component registration and pipeline processing.

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