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using-spacy-nlp

98.0
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 scripts, config templates, and FastAPI serving examples.

Commands Agents Marketplace
#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

First time? Install Skilz: pip install skilz

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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/

Need detailed installation help? Check our platform-specific guides:

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Agentic Skill Details

Type
Other
Meta-Domain
N/A
Primary Domain
N/A
Market Score
98.0

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

  • Reference files lack links back to SKILL.md making navigation one-directional
  • Quick Start example has inline comments that could be more token-efficient
  • Scope section appears after Contents but before Quick Start - unusual ordering

Recommendations

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

Graded: 1/18/2026

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 simple footer to each reference file:

---
← Back to [using-spacy-nlp](../SKILL.md)

This gives you that navigation link and bumps you +1 point toward PDA perfection.

Other Things Worth Fixing

  1. Scope section positioning – It sits between Contents and Quick Start, which is unusual. Move it after Quick Start or fold it into the metadata description. Cleaner flow, slightly better PDA.

  2. Code comment density – Your Quick Start example has inline comments like # Entities that could be trimmed or combined. spaCy docs already use these patterns, so you don't need to over-explain.

  3. Optional metadata fields – You're not using uses_llm, supported_platforms, or dependencies. Consider adding dependencies: spacy>=3.0 since that's foundational.

Quick Wins

  • Add footer links to all 5 reference files: +1 point
  • Tighten code comments in Quick Start: +0.5 points
  • Reorder Scope section: +0.5 points
  • Total upside: +2 points → potential 100/100

The skill is genuinely useful and well-structured. These are just the final polish to get it perfect.


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AI-Detected Topics

Extracted using NLP analysis

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

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