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name: textblob description: "Use TextBlob for Python text processing: tokenization, POS tagging, noun phrases, sentiment, lexical tools, classifiers, and extensions." disable-model-invocation: true metadata: disco-role: operating license: MIT

TextBlob repo skill

Use this repo skill when a task names TextBlob or asks for a simple Python NLP workflow involving raw text, tokens, sentences, part-of-speech tags, noun phrases, sentiment, spelling, WordNet, word morphology, text classifiers, or TextBlob-compatible custom models.

Do not use this skill for large neural NLP training, transformer pipelines, spaCy pipeline components, annotation platforms, or LLM evaluation unless the request specifically needs TextBlob interoperability.

Setup first

TextBlob installs as a Python package, but many workflows require NLTK corpus data at runtime. In the target environment, a typical setup is:

python -m pip install -U textblob
python -m textblob.download_corpora lite   # default models and common basics
# Use the full command when ConllExtractor or NaiveBayesAnalyzer is needed:
python -m textblob.download_corpora

Minimal import and corpus diagnostic:

python scripts/check_textblob_setup.py --json
python scripts/check_textblob_setup.py --require-all-corpora

Read corpora and setup before debugging a MissingCorpusError, optional numpy issue, or corpus-backed workflow.

Route by task

User request	Read
Tokenize text, split sentences, tag POS, extract noun phrases, compute sentiment, parse, count words/phrases, serialize sentence records, or use Blobber with built-in models	core NLP workflows
Singularize/pluralize words, stem, lemmatize, correct spelling, inspect WordNet synsets/definitions, or normalize word lists	word and lexical tools
Train/evaluate/update Naive Bayes, Decision Tree, MaxEnt, or Positive Naive Bayes classifiers; load CSV/JSON/TSV training data; write feature extractors; register data formats	classifiers and data formats
Implement custom tokenizers, POS taggers, NP extractors, sentiment analyzers, parsers, language/model extensions, or shared Blobber factories	custom models and extensions

Shared references:

• API map: package/module routing and public API family overview.
• Corpora and setup: install commands, corpus names, optional dependencies, and setup verification.
• Troubleshooting: cross-cutting install, import, corpus, and version issues.
• Repository provenance: source snapshot used to build this skill and refresh criteria.

Common decision points

• If raw text still needs tokenization, sentence splitting, tags, sentiment, or noun phrases, start with the core NLP sub-skill before moving to word-level or classifier workflows.
• If a classifier task includes file formats, labels, updates, or feature extraction, use the classifier sub-skill even if the final classifier is used through TextBlob(..., classifier=cl).
• If a request says "custom tokenizer/tagger/analyzer/parser" or mentions an extension package, use the custom-model sub-skill for interface validation, then return to core NLP for ordinary TextBlob usage.
• If a workflow fails only because corpus data is absent, do not change model selection silently. Run explicit corpus setup in the target environment and rerun the relevant smoke script.

Quick sanity examples

from textblob import TextBlob, Word

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

print(Word("speling").correct())

Classifier example:

from textblob import TextBlob
from textblob.classifiers import NaiveBayesClassifier

train = [("I love this", "pos"), ("This is awful", "neg")]
cl = NaiveBayesClassifier(train)
blob = TextBlob("I love this API. This bug is awful.", classifier=cl)
for sentence in blob.sentences:
    print(sentence, sentence.classify())

When these examples fail, first run python scripts/check_textblob_setup.py and then follow the nearest sub-skill troubleshooting reference.