---

name: "scispacy" description: "Routes scispaCy biomedical spaCy component workflows, entity-linking workflows, and project data/evaluation workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

scispaCy

scispaCy is a biomedical/scientific spaCy package. Use this skill when you need to load its models, add its custom pipes, build entity linkers, or run the project data/evaluation helpers.

Quick start

Install the package

For general use:

python -m pip install scispacy

For development inside a local checkout:

python -m pip install -e .

Install a model package

scispaCy models are installed separately from the library. Start with one of:

python -m spacy download en_core_web_sm
python -m pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.4/en_core_sci_sm-0.5.4.tar.gz

See references/installation.md for the full model list and the known-good version pairing used in this checkout.

Minimal import check

Use an isolated import check after installation:

python -I -c "import scispacy, spacy, scispacy.abbreviation, scispacy.hyponym_detector, scispacy.linking; print(scispacy.__version__)"

If you need a fuller smoke, run scripts/smoke_scispacy.py.

Route map

1) Biomedical text-processing components

Use sub-skills/pipeline-components/ for:

• combined_rule_tokenizer
• pysbd_sentencizer
• abbreviation_detector
• hyponym_detector
• WhitespaceTokenizer
• the combined-rule pipeline helper

This route is the right one when the task is about tokenization, sentence boundaries, abbreviation expansion, or Hearst-pattern hyponym extraction.

2) Entity linking and knowledge bases

Use sub-skills/entity-linking/ for:

• scispacy_linker
• CandidateGenerator
• KnowledgeBase and the built-in KB subclasses
• create_tfidf_ann_index
• UMLS semantic-type helpers and cache-backed KB loading

This route is the right one when the task is about UMLS/MeSH/GO/HPO/RxNorm linking, custom KBs, candidate generation, or ANN index creation.

3) Project data and evaluation workflows

Use sub-skills/project-workflows/ for:

• MedMentions and BIO TSV readers
• NER evaluation helpers
• UMLS export to JSONL
• frequency counting / vocabulary conversion
• package metrics summaries
• project.yml / configs/*.cfg workflow assembly

This route is the right one when the task is about training-data conversion, evaluation, or model packaging rather than the runtime pipes.

Shared references

• Read references/installation.md when you need install commands, model selection, or the verified version pairing.
• Read references/troubleshooting.md when install, import, or model-version problems appear.
• Read references/repo-provenance.md when checking whether this skill is still aligned with the current repository.
• Read references/repo-routing-metadata.json when you need the router metadata used during import and discovery.

Shared helper scripts

• Run scripts/smoke_scispacy.py to check the installed package, biomedical components, whitespace tokenization, and a tiny linker path.

Practical selection guidance

• If the request names tokenization, sentence splitting, abbreviation detection, or hyponym detection, stay in pipeline-components.
• If the request names scispacy_linker, KBs, ANN indices, UMLS, or a linker threshold, stay in entity-linking.
• If the request names MedMentions, BIO TSV, project.yml, evaluate_ner, export_umls_json, or frequency conversion, stay in project-workflows.
• If the request is only about installing the library or choosing a model package, start with references/installation.md and the smoke script.

Freshness check

Use references/repo-provenance.md to confirm the checkout and package snapshot before treating this skill as current for a different repository state.