Built an AI slop detector using your anti-pattern database

Hey — built vibecheck-slop-stopper, a tool that detects AI-generated code in PRs (GitHub Action + CLI + Claude Code skill).

Your ui-reasoning.csv anti-pattern data was one of the sources for our visual slop detection patterns (P11) — specifically the AI purple/pink gradient flagging across product categories and the product-type-specific anti-pattern severity ratings.

We also pulled from gstack's design-checklist to build a 10-item AI visual slop blacklist that maps to grep-able CSS/Tailwind patterns:

• Purple/violet/indigo gradient detection (#6366f1–#8b5cf6 range)
• Centered text density (>60% = flag)
• Uniform bubbly border-radius
• Generic hero copy ("Unlock the power of...")
• Glassmorphism blur, default AI box-shadow
• Emoji as design elements

These compile from human-readable TOML stanzas to ripgrep regex at scan time, so the detection is automated.

You're credited in our README. MIT licensed, 72 rules across 11 categories.

Just wanted to share since your data made the visual slop detection possible. Happy to answer any questions about how we used it.

The issue only shares the external vibecheck-slop-stopper project and explains how it used ui-reasoning.csv and gstack's design-checklist. Review the linked repository and this project's referenced data to determine whether any documentation or attribution change is requested; no files, tests, entry points, or definition of done are provided.