data-export-excel

I took a look at your data-export-excel 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 puts you in D territory—needs work, but definitely salvageable. The skill is grounded in real utility (your bioinformatics examples are solid), but the structure and presentation could use some tightening. Your strongest area is Utility (15/20), but Progressive Disclosure Architecture is dragging you down at just 14/30. This is based on Anthropic's skill best practices rubric.

What's Working Well

• Strong examples – Your QC metrics, DEG analysis, and marker gene examples actually show real use cases. That's the kind of concrete stuff that makes skills useful.
• Clear workflow – The 5-step numbered process is easy to follow, and the step-by-step breakdown works well for people unfamiliar with openpyxl.
• Good trigger terms – You mention "export", "Excel", "spreadsheet" naturally throughout, which helps discoverability even if they're not formally declared.

The Big One: Monolithic File Structure

Your entire skill is crammed into a single 244-line SKILL.md file. That's the main thing holding you back—you're losing 8+ points here alone.

Why it matters: Longer files are harder to navigate, they tank token efficiency (which matters for skill activation), and they violate the Progressive Disclosure Architecture principle. The idea is that people should be able to understand your skill quickly at a glance, then dive deeper if they need to.

The fix: Split this into a layered structure:

• Keep SKILL.md under 60 lines (just overview, basic usage, and key examples)
• Create references/formatting-guide.md for advanced formatting options
• Creat...