avoid-ai-design
Removes telltale AI-generated design artifacts that damage brand credibility and user trust.
A Claude Code skill that audits AI-generated frontend and rewrites it to remove generic AI-slop design patterns (purple gradients, Inter, default shadcn). The design counterpart to avoid-ai-writing.
- Ask Claude to audit frontend code and identify generic AI design patterns like purple gradients.
- Generate improved CSS and component code that replaces default shadcn styling with custom designs.
- Find and remove overused fonts like Inter and placeholder design elements from React components.
Removes telltale AI-generated design artifacts that damage brand credibility and user trust. Helps teams maintain consistent, intentional visual identity rather than shipping commodity aesthetics.
Design-conscious engineering teams and product leads auditing AI-assisted frontend code before production.
https://github.com/funboy322/avoid-ai-design
By funboy322
How to Get It
claude plugins install funboy322/avoid-ai-design
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
After installing, paste this into Claude:
Audit frontend code and identify generic AI design patterns like purple gradients
Trust Signals Auto-scanned
Community Pulse New
No community discussions found yet. This doesn't mean the tool isn't good — it may be new or serve a niche use case.
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 34 GitHub stars; 1 contributors; last commit 37d ago; license MIT.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
- Single maintainer. Consider the risk if this person stops maintaining the project.
How to evaluate tools before deploying →
Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.
Evaluation
Scored from trust signals (evidence-eval-v1): 34 GitHub stars; 1 contributors; last commit 37d ago; license MIT.