Best fit
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.
// Web & App UI Design
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
input Product: incident-response dashboard for small operations teams
Current state: dark gradient hero, four equal glass cards, purple accents, oversized rounded controls
Keep: route structure, data tables, logo, and current feature set
Goal: feel calm, operational, and evidence-led rather than like a generic AI SaaS template
result Diagnosis: equal card weight and decorative gradients hide incident priority; oversized radii make dense operational controls feel promotional.
Rules: use one severity accent at a time, reduce radius on data surfaces, tighten table density, reserve the largest type for the active incident, and replace decorative copy with observable status language.
Patch order: tokens, incident summary, severity states, then secondary cards.
Proof: capture the same incident fixture at 1440px and 375px before and after.
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.
// choose with context
Is AI UI De-Templater the right skill?
It returns
Evidence-linked generic-pattern diagnosis. Product-specific visual rules and token changes.
Do not use it as
It does not manufacture a brand strategy from no product or audience context.
Compatibility, access, version, and licence
- Reads or accesses
- Declared local scope: src/**, app/**, pages/**, components/**.
- Compatibility
- Screenshot-led review for any web interface. HTML and CSS source, including token-based styling systems. React, Next.js, and Vite components when source is supplied.
- Version
- 1.0.1
- Licence
- Review the package licence and seller terms at the linked destination.
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