Han Design Drops the Configuration Quiz and Adds an Autopilot for AI-Generated Pages
Agent skills that pile on features without absorbing the decision cost fail in practice. This refactor shows the real product work is not adding more CSS or components but designing the agent's internal decision sequence so a user can say "make it look better" and get a finished, reviewed page.
A design skill for AI coding agents stopped bombarding users with configuration questions about themes, fonts, and components. Instead, it now scans the project context to generate an internal design brief and gets straight to building. The update introduces a 0–3 visual intensity scale that acts as a decoration budget, six complete page starters to replace ad-hoc component assembly, and automatic content drafting that respects a hard factual red line against inventing brand data. A new browser-based self-review step catches contrast failures and mobile overflow that static checks miss, and the default prompt was rewritten as a six-step executable workflow that requires re-rendering and re-checking until issues are actually fixed, not just reported.
Capability accumulation without decision-cost absorption is the core failure mode for AI design tools: users want outcomes, not a product-manager certification in the tool's domain.
Framing visual intensity as a budget rather than a component picker gives the agent a transferable constraint that works across themes and page types without exposing implementation details.
Static code checks are insufficient for visual QA; real browser rendering at target breakpoints surfaces contrast, overflow, and attention-hierarchy problems that linting cannot see.
Agents interpret "review and fix" as "report findings and stop" unless the instruction explicitly ties problem discovery to a mandatory re-render-and-recheck loop.
Content scaffolding with a strict factual red line is a practical middle ground: structure can be guessed, but brand facts cannot be invented without corrupting trust.