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name: install-and-inspect description: "Install and inspect spaCy environments with import, version, blank-pipeline, CLI, model-package, and optional backend probes." disable-model-invocation: true metadata: disco-role: operating license: MIT

install-and-inspect

Use this sub-skill when the task is to install the public spacy package, prove that it imports, inspect spacy.__version__, validate a blank English pipeline, check the python -m spacy CLI entry point, load an already-installed pipeline package/path, or probe optional extras/backends without downloading pretrained models by default.

Fast route

1. Read references/install-and-inspect.md when choosing pip, conda, or source-install patterns and when running base import, blank tokenizer, model-load, info, validate, or CLI-help checks.
2. Read references/optional-dependencies-and-backends.md when the user asks about lookups, transformers, CUDA/CuPy variants, Apple acceleration, or Japanese/Korean/Thai tokenizer extras.
3. Read references/troubleshooting.md when imports fail, compiled extensions mismatch, spacy.load() cannot find a model, validate reports stale pipelines, optional extras are missing, GPU selection is unclear, or the spacy console command is not on PATH.
4. Run scripts/spacy_healthcheck.py for a safe installed-package smoke check from any current working directory; use --model, --prefer-gpu, --require-gpu, or --run-validate only when those checks are explicitly needed.

Minimal default procedure

Use python -m spacy instead of a bare spacy shell command when the active Python environment is uncertain.

python -m pip install -U pip setuptools wheel
python -m pip install -U spacy
python -m spacy --help
python -m spacy info --silent
python scripts/spacy_healthcheck.py

For a no-download API smoke, prefer spacy.blank("en") over spacy.load("en_core_web_sm"). Use spacy.load() only after a trained pipeline package or local pipeline directory exists.

Boundaries and routes

• Full training, config lifecycle, spacy train, spacy debug, spacy evaluate, and spacy package belong to the training-and-cli sub-skill.
• Doc, Token, Span, tokenizer customization, matchers/rulers, serialization, scoring, and displaCy belong to the documents-and-visualization sub-skill.
• Custom components, factories, pipeline assembly/order, registry wiring, and pipe analysis belong to the pipeline-components sub-skill.
• spacy project templates, assets, run/dry-run, remotes, and DVC workflows belong to the project-workflows sub-skill.
• Maintainer release/version shell scripts are excluded from runtime use; inspect the installed public package instead.

Evidence basis

This sub-skill is distilled from the package README, packaging metadata, public top-level APIs, CLI documentation/source, installed-package inspection, backend verification planning, native candidate mapping, and source-script inventory for the spaCy checkout. Runtime instructions are self-contained and do not require reopening the source checkout.