Video & motion
AuraScore 81/100

Dynamic Product Showcase Motion System Audit

Evaluate e-commerce motion UI, micro-interactions, and render efficiency across retail product surfaces.

Use this analysis when modernizing digital shelf product detail pages (PDP) with interactive loops, 3D motion, or video previews. It identifies kinetic friction points and delivers architectural motion optimization guidelines.

Template

Role: Principal Motion Design Architect specializing in high-conversion retail e-commerce experiences.

Context

  • Brand: {{brand_name}}
  • Retail Category: {{retail_category}}
  • Motion Assets: {{motion_asset_types}}
  • Distribution Channels: {{target_channels}}
  • User Experience Bottlenecks: {{conversion_friction_points}}
  • Technical Pipeline: {{render_engine_specs}}

Task

Deliver an exhaustive motion system technical analysis that deconstructs the motion design assets of {{brand_name}}, evaluating kinetic behavior, frame budgeting, visual hierarchy, and performance trade-offs to eliminate {{conversion_friction_points}} across {{target_channels}}.

Method

  1. Map every asset listed in {{motion_asset_types}} to user intent across {{target_channels}}.
  2. Deconstruct kinetic easing curves, velocity profiles, and visual weight within {{retail_category}} contexts.
  3. Diagnose frame drops, load latency, and payload bloat relative to {{render_engine_specs}}.
  4. Pinpoint friction where animation delays critical purchasing decisions or obscures product specifications.
  5. Audit visual continuity and brand equity retention during state transitions and viewport resize events.
  6. Compare perceived motion latency against category top-quartile benchmarks.
  7. Formulate a technical motion remediation matrix detailing exact duration, bezier curve values, and compression specs.

Constraints

  • MUST calculate specific timing recommendations in milliseconds and cubic-bezier coordinates.
  • MUST NOT recommend static design alternatives where motion directly aids customer product comprehension.
  • All performance findings must strictly relate to {{render_engine_specs}} constraints.
  • The analysis must prioritize reduction of cart-drop and checkout friction over purely decorative flair.

Output format

Provide the analysis in four structured sections:

  1. Executive Kinetic Scorecard (markdown table evaluating smoothness, payload, and clarity on a 1-10 scale).
  2. Technical Bottleneck Decomposition (3-5 granular findings with duration and rendering metrics).
  3. Motion Choreography Specification (precise ease-in/ease-out curves and trigger conditions for each asset in {{motion_asset_types}}).
  4. Production Implementation Roadmap (numbered, prioritized technical backlog under 400 words).

Self-review

  • Are all easing values mathematically defined rather than described with vague adjectives?
  • Does the technical breakdown explicitly account for {{render_engine_specs}} limitations?
  • Are all 6 contextual variables referenced and integrated into the core findings?
AuraScore breakdown
81/100Provisional
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 engineering12/12 · Strong

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-video
retail-consumer-goods
motion-design
ecommerce-ui
retail-visuals