AI UI De-Templater
Replace generic AI-interface patterns with product-specific visual rules and a bounded implementation plan that preserves working behavior.
What problem does AI UI De-Templater solve?
A functional interface looks interchangeable with generic AI-generated products because its typography, spacing, color, shape, density, and hierarchy do not express the product's actual job.
Use it to
- Diagnose why an AI-built interface looks interchangeable with other template products
- Replace decorative gradients, equal-weight cards, and excessive radii with product-specific rules
- Translate a product promise into bounded token and component changes
- Create a comparable review board using the same route, state, content, and viewport
What it returns
- Evidence-linked generic-pattern diagnosis
- Product-specific visual rules and token changes
- Prioritized component patch plan or approved patch
- Before-and-after comparison board
A representative input and result
Access and approval boundaries
- Read access to supplied screenshots, source files, product context, and brand references.
- Optional browser access for computed-style and viewport inspection.
- Any source write, dependency installation, or deployment requires separate approval.
Known limitations
- Weak or contradictory product positioning limits how specific the visual system can become.
- The skill does not create a full identity system, logo, or original illustration library.
- A visual recommendation remains a design judgment until the owner reviews the comparison in the real product context.
Questions
What counts as a generic AI UI pattern?
The skill looks for repeated visual and content decisions that are not justified by the product, such as interchangeable gradients, equal-weight cards, excessive rounded surfaces, vague headings, and decorative hierarchy.
Does it redesign the whole application?
No. It defines a small set of product-specific rules and prioritizes changes with the greatest effect while preserving working structure.
Do I need an existing brand guide?
No, but product audience, promise, constraints, and at least one trustworthy reference make the recommendations more specific.
Can it work from screenshots alone?
Yes for diagnosis and visual direction. Source is needed for precise token and component patches.
How is this different from AI UI Finish Loop?
Finish Loop completes missing and broken states. De-Templater changes generic visual decisions even when all states technically exist.
What proof is included?
The result includes a comparison board built from the same content, viewport, and state so the owner can review the actual effect of the changes.
AI UI De-Templater keeps proof and approval boundaries visible.
The listing includes the tested package, realistic samples, declared permissions, and known limitations.
Get AI UI De-Templater on Agensi