Spacy
spaCy NLP library with pipelines. Use for text processing.From its SKILL.md
npx -y skills add G1Joshi/Agent-Skills --skill spacyAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 12 stars12 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
1.0 KB, 235 tokens by cl100k_base, as published. Nobody here has run it
spaCy
spaCy is "Industrial Strength" NLP. Unlike NLTK (academic), spaCy focuses on providing the best single algorithm for a task. v3.8 supports Python 3.13.
When to Use
- NER (Named Entity Recognition): Extracting person names, dates, orgs.
- Parsing: Dependency parsing to understand sentence structure.
- Speed: Cython-optimized pipelines.
Core Concepts
Pipeline
Tokenizer -> Tagger -> Parser -> NER.
Doc / Token / Span
The core data structures. Efficient memory usage.
Prodigy
The annotation tool (paid) from the same creators, tightly integrated.
Best Practices (2025)
Do:
- Use Transformer pipelines:
en_core_web_trf(Roberta-based) for high accuracy. - Use
nlp.pipe(): For batch processing huge texts.
Don't:
- Don't use for GenAI: spaCy is for structure extraction, not text generation (LLMs).
References
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.