Find agent skills by outcome

process: Simulate user eye movements based on established cognitive science principles

aiming for 85-90% predictive accuracy compared to real human data.

negative_prompt: text

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reference_authority: [Nielsen Norman Group (NN/g)

Cognitive Load Theory

* Spatial Audio: 3D sound map

reverb tail

* Ambient Sounds: Room tone

atmospheric layers (wind

directive: Identify human faces or eyes. These areas receive immediate

highest-intensity focus (hottest red zones within milliseconds).

Data

technicalmetadatahypothesis: Arri Alexa

35mm Grain

directive: Identify elements with high contrast or unique visual weight (e.g.

primary CTAs like a 'Create' button). These must be marked as high-priority fixation points.

* Visual Anchor Points: Saccadic eye movement prediction (where the eye lands 1st

2nd).

* Micro-Beats & Emotion: Breakdown of action into seconds (e.g.

00:01 turns head). Analysis of internal feelings and body language.

* Costume & Styling: Fabric textures (leather

silk)

* Atmospherics: Fog

smoke

* Camera Movement: Precise movement (Static

Pan

* Props & Decor: Analysis of objects (clutter

hero props

* Framing & Lensing: Focal length (24mm

50mm

* Transition Logic: Connection to potential previous/next shots (Match cut

J-Cut).

Creative

* Set Design & Architecture: Physical space description

architectural style (Brutalist

* Material Physics: Specific textures (rust

chrome

* Optical Characteristics: Lens flares

chromatic aberration

Creative

* Lighting Design: Key

Fill

* Rhythm & Tempo: Pacing (Largo

Allegro

Lens:* (e.g.

Anamorphic

* Object Inventory: List distinct key objects present (e.g.

1 vintage Rolex watch

Settings:* (Est. ISO

Shutter Angle