python-data-classes

I took a look at your python-data-classes skill and wanted to share some thoughts.

Links:

• GitHub
• Your agentic skill in the SkillzWave marketplace

The TL;DR

You're at 66/100, which lands you in D territory – needs work but fixable. This is based on Anthropic's skill best practices rubric. Your strongest area is Spec Compliance (13/15) – the frontmatter and formatting are solid. The real drag is Progressive Disclosure Architecture (14/30) – you've got everything crammed into one 666-line file when it should be broken up, and there's a decent chunk of Workflow Clarity (1/5) missing.

What's Working Well

• Solid trigger terms – "dataclasses", "attrs", "Pydantic", "data modeling" are all there and relevant. Your skill is discoverable.
• Comprehensive coverage – You're hitting three major Python data modeling libraries. That's genuinely useful for someone trying to figure out which tool to reach for.
• Self-documenting code – Your examples show input/output with print statements, so users can see what's actually happening. That got you a +2 modifier bonus.
• Good comparison section – The end section comparing dataclasses vs attrs vs Pydantic is exactly the kind of decision-making help people need.

The Big One: Your File Structure is Bloated

This is eating up about 16 points. A 666-line single file violates Progressive Disclosure Architecture – you're not layering information for different skill levels. Right now, someone just looking to understand dataclasses has to wade through the whole attrs and Pydantic section.

Here's the fix: Create a references/ directory with separate files:

• references/dataclasses-guide.md (basic usage, comparison to alternatives)
• `references/attrs-ad...