General design
AuraScore 79/100

Internal Knowledge Platform Visual Ergonomics Assessment

Assess the visual ergonomics, interface clutter, and layout hierarchy of internal enterprise knowledge bases.

Utilize this template when diagnosing usability and visual design issues within company intranets, documentation wikis, or HR operational portals. It yields a targeted analysis of visual noise, layout patterns, and task efficiency.

Template

Role: Principal Enterprise UX Architect specializing in employee productivity platforms and visual ergonomics.

Context

  • Organizational Footprint: {{organization_scale}}
  • Platform Type: {{knowledge_repository_type}}
  • Existing Visual Layout: {{current_layout_structure}}
  • Primary Employee Personas: {{employee_user_personas}}
  • Known Navigation Friction: {{information_architecture_issues}}
  • Operational Targets: {{productivity_goals}}

Task

Conduct a rigorous visual ergonomics and interface design analysis of the internal knowledge system to diagnose visual clutter, improve scan efficiency, and align interface components with {{productivity_goals}}.

Method

  1. Deconstruct {{current_layout_structure}} to evaluate visual density, cognitive noise, and the proportion of interface chrome versus primary content.
  2. Analyze visual grouping and gestalt principles (proximity, similarity, continuity) within content containers, cards, and metadata tags.
  3. Assess the typographic system across long-form reading, code blocks, reference tables, and inline operational notices.
  4. Map the visual search patterns used by {{employee_user_personas}} when attempting rapid lookup tasks under time constraints.
  5. Correlate reported {{information_architecture_issues}} with interface-level failures such as low contrast, visual competition between banners, or ambiguous iconography.
  6. Evaluate responsive behavior and layout adaptability across typical workplace hardware environments.
  7. Develop actionable UI layout recommendations to streamline scannability and support {{productivity_goals}}.

Constraints

  • MUST anchor every UI critique in measurable employee ergonomics and visual search speed.
  • MUST NOT suggest cosmetic overhaul without demonstrably resolving {{information_architecture_issues}}.
  • Design recommendations must be feasible within the technical constraints of standard {{knowledge_repository_type}} platforms.
  • Avoid generic UI recommendations by tailoring layout patterns to {{employee_user_personas}} workflows.

Output format

Structure the visual analysis into four distinct sections:

  1. Visual Ergonomics Diagnostic: structured breakdown of visual clutter, signal-to-noise ratio, and scan efficiency.
  2. Persona-Specific Ergonomic Pain Points: matrix linking each group in {{employee_user_personas}} to specific UI layout bottlenecks.
  3. Component-Level Visual Recommendations: detailed analysis of 4 key components (Search Bar, Article Headers, Navigation Sidebar, Metadata Tags) with explicit before/after layout rules.
  4. Impact Projections: concise summary connecting suggested design changes to target metrics in {{productivity_goals}}.

Self-review

  • Did I address the visual challenges of all employee cohorts listed in {{employee_user_personas}}?
  • Are component recommendations specific enough to be translated directly into UI component specs?
  • Does the analysis address operational efficiency rather than just aesthetic taste?
AuraScore breakdown
79/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering10/12 · Adequate

Hard boundaries — what the model must and must not do.

Output specification6/14 · Thin

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency5/10 · Thin

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness3/5 · Adequate

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

design-visual
design-general
research-productivity-operations
visual ergonomics
knowledge base
enterprise ux