---

name: stanza description: "Use Stanza for neural NLP pipelines, CoreNLP client workflows, CoNLL-U documents, training data, and demos." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Stanza Repo Skill

Use this skill when a task involves the stanza Python NLP package from the Stanford NLP Group: neural NLP pipelines, model resources, CoNLL-U documents, Java Stanford CoreNLP access, Stanza model training/data preparation, or demo/visualization adaptation.

First checks

• Install/use a normal Python package environment with stanza importable.
• Minimal import smoke:

import stanza
from stanza import Pipeline, MultilingualPipeline, Document
from stanza.utils.conll import CoNLL
print(stanza.__version__)

• Run scripts/check_environment.py --help for a no-download diagnostic helper.
• Read references/repo-provenance.md before deciding whether this skill is current for a checkout.
• Read references/troubleshooting.md when install/import, resource, optional dependency, CoreNLP, or training setup fails.

Route by task

Task intent	Read
Build or debug stanza.Pipeline, MultilingualPipeline, processor lists, model packages, model caches, downloads, offline mode, GPU/CPU selection, batching, or streaming	sub-skills/pipelines-and-resources/SKILL.md
Parse, write, validate, or manipulate Stanza Document, Sentence, Token, Word, NER/coref fields, serialized docs, or CoNLL-U	sub-skills/documents-and-conllu/SKILL.md
Use Java Stanford CoreNLP through Stanza: CoreNLPClient, server lifecycle, external server mode, properties, output formats, Semgrex/TokensRegex/Tregex/Ssurgeon/Tsurgeon, protobuf	sub-skills/corenlp-client/SKILL.md
Plan custom model training, evaluation, data conversion, pretrains, charlms, training wrappers, UD/NER/constituency/coref data roots, or safe training command construction	sub-skills/training-and-data-prep/SKILL.md
Adapt demos, notebooks, dependency/NER visualization, Streamlit/browser visualizers, or produce static HTML from existing annotations	sub-skills/visualization-and-demos/SKILL.md

Package entry points to remember

• stanza.download(lang=..., processors=..., package=..., model_dir=..., proxies=...) stages model resources.
• stanza.Pipeline(lang='en', processors='tokenize,pos,lemma,depparse', download_method=...) builds the neural pipeline.
• stanza.MultilingualPipeline(...) detects language and routes batches to per-language pipelines.
• stanza.Document and stanza.utils.conll.CoNLL handle document objects and CoNLL-U conversion.
• stanza.server.CoreNLPClient talks to a Java Stanford CoreNLP server.
• python -m stanza.models.<module> and python -m stanza.utils.training.run_<task> expose training/evaluation helpers.

Safety and boundaries

• Model downloads, CoreNLP distribution downloads, word-vector downloads, notebook execution, Java server startup, W&B logging, and full model training are side-effectful. Make them explicit and obtain task approval before running them.
• Do not assume GPU is required. Stanza supports CPU for functional workflows; CUDA is optional acceleration unless a user specifically asks for GPU validation.
• Do not use this runtime skill as a link farm back to a source checkout. The references and scripts here are self-contained for Stanza 1.14.0 behavior.

References

• references/package-overview.md summarizes package surfaces, dependencies, and extras.
• references/troubleshooting.md covers cross-cutting failures and routes to sub-skill troubleshooting.
• references/repo-provenance.md records the source snapshot and evidence paths.
• references/repo-routing-metadata.json provides structured router metadata for managed import tooling.