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
- Check the exact component signature in the reference.
- Decide whether the user needs a stateless component or a stateful factory.
- Use the bundled registry inspection script to confirm the named component exists.
- 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.