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name: core-nlp-workflows description: "Use TextBlob document-level NLP workflows for tokenization, sentence splitting, POS tags, noun phrases, sentiment, parsing, counts, and Blobber routing." disable-model-invocation: true metadata: disco-role: operating license: MIT

TextBlob core NLP workflows

Use this sub-skill when a task starts from raw text and needs TextBlob's high-level document or sentence APIs: TextBlob, Sentence, Blobber, words, tokens, sentences, POS tags, noun phrases, sentiment, parsing, n-grams, counts, JSON serialization, or built-in model overrides.

Route elsewhere when the request is mostly about:

• Word-level spelling, inflection, lemmatization, stemming, WordNet, or WordList batch transforms: ../word-and-lexical-tools/SKILL.md.
• Classifier training/evaluation, training data files, feature extractors, or TextBlob(..., classifier=...): ../classifiers-and-data-formats/SKILL.md.
• Writing custom model classes, extension packages, or reusable Blobber factories with custom components: ../custom-models-and-extensions/SKILL.md.

Bundled resources

• Workflows: task-oriented recipes for tokenization, tagging, noun phrases, sentiment, parsing, counts, JSON, and Blobber.
• API reference: verified signatures, defaults, return shapes, and model classes for document-level APIs.
• Troubleshooting: missing NLTK corpora, punctuation/tokenization surprises, slow corpus-backed models, and analyzer or parser misuse.
• Core NLP smoke script: read-only installed package checks. Run python scripts/core_nlp_smoke.py --help.

Operating workflow

1. Confirm setup. TextBlob import alone is not enough for corpus-backed workflows. If tags, sentence splitting, noun phrases, lemmatization, or the Naive Bayes sentiment analyzer are needed, ensure the installed environment has TextBlob corpora. The parent skill links a setup reference and a root setup diagnostic.
2. Create a blob. Use TextBlob(text) for ordinary raw text. The constructor validates tokenizer, POS tagger, NP extractor, analyzer, and parser objects when custom ones are supplied.
3. Choose the right property.
   • Use .sentences for Sentence objects and sentence-level processing.
   • Use .words for word tokens without punctuation.
   • Use .tokens or .tokenize(tokenizer) when punctuation or a custom tokenizer contract matters.
   • Use .tags/.pos_tags for flattened POS tags across sentences.
   • Use .noun_phrases for normalized lowercase noun phrase strings.
   • Use .sentiment, .polarity, .subjectivity, or .sentiment_assessments for built-in sentiment workflows.
   • Use .parse() for the pattern-style parse string.
   • Use .word_counts, .np_counts, and .ngrams(n) for simple counts and phrase windows.
4. Use Blobber when models should be shared. Blobber(...) creates TextBlob instances that reuse the same tokenizer, tagger, extractor, analyzer, parser, and classifier objects. This is useful for expensive or configured models; route to the custom-model sub-skill when implementing the components themselves.
5. Validate with a tiny smoke. Run the bundled smoke script before relying on a new environment or after debugging corpus paths. Use --skip-corpus-heavy only when POS, sentences, and noun phrases are not part of the task.
6. Keep downstream outputs explicit. TextBlob properties are convenient and often cached. When building pipelines, record which property produced each field, whether punctuation was kept, and whether corpora or optional models were required.

Quick patterns

from textblob import Blobber, TextBlob
from textblob.sentiments import NaiveBayesAnalyzer
from textblob.tokenizers import WordTokenizer

blob = TextBlob("TextBlob is amazingly simple. Great fun!")
[str(s) for s in blob.sentences]
[(str(word), tag) for word, tag in blob.tags]
blob.noun_phrases
blob.sentiment.polarity
blob.ngrams(2)

# Discrete sentiment analyzer trained from movie_reviews corpus.
TextBlob("I love this library", analyzer=NaiveBayesAnalyzer()).sentiment

# Shared model factory.
tb = Blobber(tokenizer=WordTokenizer())
first = tb("one text")
second = tb("another text")
assert first.tokenizer is second.tokenizer

If .tags, .sentences, or .noun_phrases raises MissingCorpusError, do not silently switch algorithms. Use references/troubleshooting.md to identify the missing corpus and run the explicit setup command in the target environment.