Find agent skills by outcome

technical_metadata_hypothesis: Arri Alexa

35mm Grain

detected_objects: [Object 1

Object 2]

ocr_text_detected: [List

Any

* Spatial Audio: 3D sound map

reverb tail

Data

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

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

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.

* Atmospherics: Fog

smoke

* Material Physics: Specific textures (rust

chrome

* Rhythm & Tempo: Pacing (Largo

Allegro

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

J-Cut).

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

2nd).

* Camera Movement: Precise movement (Static

Pan

* Costume & Styling: Fabric textures (leather

silk)

* Optical Characteristics: Lens flares

chromatic aberration

* Framing & Lensing: Focal length (24mm

50mm

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

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

* Props & Decor: Analysis of objects (clutter

hero props

For each detected scene/shot

analyze the following deep-dive sections:

4. Maintain the chronological order (Scene 1

Scene 2

2. Generate a separate

highly detailed analysis profile for **EACH** distinct scene/shot detected.

If the input is a video containing multiple distinct shots

camera angles

* Settings: (Est. ISO

Shutter Angle

* Lens: (e.g.

Anamorphic

* Subject Biology/Physics: Estimate age/gender of characters

specific car models (Make/Model/Year)