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using-spacy-nlp

98
A

Industrial-strength NLP with spaCy 3.x for text processing and custom classifier training. Use when "installing spaCy", "selecting model for nlp" (en_core_web_sm/md/lg/trf), "tokenization", "POS tagging", "named entity recognition" (NER), "dependency parsing", "training TextCategorizer models", "troubleshooting spaCy errors" (E050/E941 model errors, E927 version mismatch, memory issues), "batch processing with nlp.pipe", or "deploying nlp models to production". Includes data preparation scrip...

CommandsAgentsMarketplace
#training#references#spaCy#Production Deployment#processing#text processing#agentic-skill#production

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Installation for Agentic Skill

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skilz install SpillwaveSolutions/spacy-nlp-agentic-skill/using-spacy-nlp
skilz install SpillwaveSolutions/spacy-nlp-agentic-skill/using-spacy-nlp --agent opencode
skilz install SpillwaveSolutions/spacy-nlp-agentic-skill/using-spacy-nlp --agent codex
skilz install SpillwaveSolutions/spacy-nlp-agentic-skill/using-spacy-nlp --agent gemini

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Download Agent Skill ZIP

Extract and copy to ~/.claude/skills/ then restart Claude Desktop

1. Clone the repository:
git clone https://github.com/SpillwaveSolutions/spacy-nlp-agentic-skill
2. Copy the agent skill directory:
cp -r spacy-nlp-agentic-skill/skills/using-spacy-nlp ~/.claude/skills/
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Need detailed installation help? Check our platform-specific guides:

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Agentic Skill Details

Owner
SpillwaveSolutions (GitHub)
Repository
spacy-nlp-agentic-skill
Type
Other
Meta-Domain
Primary Domain
Market Score
98
Agentic Skill Grades →

Agent Skill Grade

A
Score: 98/100 Click to see breakdown

Score Breakdown

Spec Compliance
13/15
PDA Architecture
28/30
Ease of Use
23/25
Writing Style
9/10
Utility
19/20
Modifiers: +6

Areas to Improve

  • Missing back-navigation in references
  • Code comments could be tighter
  • Scope section placement

Recommendations

  • Add trigger phrases to description for discoverability
  • Add table of contents for files over 100 lines

Graded: 2026-01-19

Developer Feedback

I took a look at your using-spacy-nlp skill and wanted to share some thoughts.

Links:

The TL;DR

You're at 98/100 – that's A-grade territory. This is solid work that follows Anthropic's best practices. Your strongest area is Utility (19/20) – the skill actually solves real problems developers face with spaCy. The only things holding you back are minor navigation and organization tweaks that would push you to perfect.

What's Working Well

  • Reference architecture is chef's kiss – You've got 5 references, 4 scripts, and 2 assets all at one level deep. Zero nesting, perfect separation of concerns. That's exactly how PDA should work (28/30 score).
  • Trigger phrases are comprehensive – Installation, NER, POS tagging, TextCategorizer, troubleshooting (E050/E941 errors), batch processing with nlp.pipe, production deployment. You've actually thought about how people search for this.
  • Practical examples that matter – Your scripts aren't toy code. Data prep, config templates, FastAPI serving, evaluation with metrics. These are real-world patterns developers need.
  • Token economy is tight – SKILL.md at 333 lines, every section earns its space. You're not padding things out (9/10 on token economy).

The Big One

Missing back-navigation from references to SKILL.md – Right now when someone's in references/text-classification.md or references/production.md, they can't easily get back to the main skill overview. It's a one-way trip.

Why it matters: Navigation signals are part of your PDA score, and this breaks discoverability flow. Someone deep in a reference might want to jump back to see the big picture or navigate to a different reference.

The fix: Add a ...

AI-Detected Topics

Extracted using NLP analysis

training references spaCy Production Deployment processing text processing agentic-skill production NLP Industrial-strength NLP

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