critique

Evaluate interfaces for UX quality, accessibility, and AI-generated anti-patterns.

Updated Apr 17, 2026
One-click install
npx skills add https://github.com/djkloop/n --skill critique-djkloop
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: critique
Source: https://github.com/djkloop/n/tree/main/.trae/skills/critique
Command: npx skills add https://github.com/djkloop/n --skill critique-djkloop

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

The critique skill provides a rigorous, director-level evaluation of interfaces to identify visual hierarchy failures, information architecture issues, cognitive overload, AI-generated anti-patterns, and accessibility gaps so teams can prioritize fixes and improve user outcomes.

Core Features & Use Cases

  • Dual assessment workflow: Runs an independent LLM design review and a deterministic automated detector to avoid bias and surface both subjective and mechanical issues.
  • Quantified outputs: Produces Nielsen heuristic scores, cognitive load counts, AI-slop verdicts, prioritized P0–P3 issues, persona-specific red flags, and concrete remediation commands.
  • Use Case: Use when shipping a landing page, component library, or feature to get a single, prioritized report combining human-style critique with reproducible detector findings.

Quick Start

Ask the critique skill to review the page or design files (or paste a URL) and produce a combined LLM review and automated detector report with heuristic scores, cognitive load assessment, AI-slop detection, prioritized fixes, persona red flags, and recommended remedial commands.

Frequently Asked Questions about critique

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run a heuristic design review on a webpage?

To run a heuristic design review on a webpage, paste the URL or design artifact. The system produces Nielsen heuristic scores, cognitive load counts, and P0–P3 prioritized issues combining subjective LLM critique with deterministic automated detector findings.

What is cognitive load assessment in UX evaluation?

Cognitive load assessment in UX evaluation identifies information architecture issues and visual hierarchy failures that overwhelm users. It generates failure counts to help teams prioritize remediation and improve user outcomes.

Can I detect AI-generated anti-patterns in UI components?

Yes, you can detect AI-generated anti-patterns in UI components. The evaluation provides AI-slop verdicts and persona-based red flags alongside visual inspection and source analysis to surface mechanical issues.

Does automated accessibility detection work with design artifacts?

Automated accessibility detection works with design artifacts, webpages, or individual components. It applies deterministic detectors and overlay-based findings alongside an independent LLM design review to avoid bias.

What is the best way to prioritize UX accessibility fixes?

The best way to prioritize UX accessibility fixes is using P0–P3 severity ratings. The review outputs specific remediation commands and persona-based red flags, allowing teams to address visual hierarchy and accessibility gaps effectively.

Are there limitations to combining LLM design review with deterministic detectors?

A limitation of combining LLM design review with deterministic detectors is the reliance on available source analysis and overlay-based findings. Without accessible source code, automated detection may produce incomplete accessibility or cognitive load assessments.