AI-Generated UI Always Looks the Same — This Reasoning Engine Injects Industry Design Sense
AI coding assistants ship UI that is visually homogeneous and often breaks basic accessibility and interaction rules. A reasoning engine that injects industry design constraints before code generation cuts the prompt-tweaking cycle from ten rounds to one, and produces a reusable design token file that keeps a multi-page project visually coherent.
AI coding tools produce competent code but visually generic, often unusable interfaces: purple gradients on financial products, missing hover states, and contrast ratios that fail accessibility standards. The problem is that large models average all the web pages they've seen and lack industry-specific design knowledge — they don't know that finance bans certain color palettes or that a SaaS dashboard needs particular interaction states.
ui-ux-pro-max-skill works as a design reasoning engine that runs locally alongside Cursor, Claude Code, and other AI editors. It retrieves from 161 industry rule sets, 67 UI styles, and 57 font pairings in parallel, then filters combinations through a reasoning engine before outputting a complete design system plus a pre-delivery checklist. A rebuild of a financial SaaS login page swapped the generic gradient for a deep-blue palette (`#1E3A8A`), added full hover and focus states, and produced a MASTER.md design token file reusable across an entire project.
The tool is MIT-licensed, runs locally with no cloud telemetry, and supports React, Vue, Next.js, Flutter, and other stacks. First-time generation adds 30–60 seconds of reasoning overhead, and output is an engineering prototype — not pixel-level Figma fidelity — so manual detail tweaking is still needed. Niche verticals may fall back to generic templates unless custom CSV rule files are added.
The core insight is not that AI can't design — it's that AI lacks the domain-specific constraints real designers internalize. Feeding those constraints as structured knowledge before generation changes output from 'looks like a webpage' to 'looks like a finance webpage.'
Persisting a MASTER design system file and per-page override files solves a real multi-page consistency problem that ad-hoc AI prompting cannot — every new page in a project pulls from the same tokenized baseline.
The anti-pattern blacklist approach (explicitly banning known AI aesthetic failures per industry) is more reliable than hoping a prompt will steer the model away from them — it removes bad options from the output space entirely.
The 30–60 second reasoning overhead on first generation is a meaningful latency cost for rapid prototyping workflows, and the tool's value proposition hinges on whether that upfront cost is repaid by fewer iteration rounds.