General
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* *Lens:* (e.g.
Anamorphic
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Find agent skills by outcome
132,787 skills indexed with the new KISS metadata standard.
Showing 24 of 132,787Categories: Education, General, Coding & Debugging, Data, Cursor-rules
General
PromptBeginner5 minmarkdownQuality: 20
* **Framing & Lensing:** Focal length (24mm
50mm
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General
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* **Micro-Beats & Emotion:** Breakdown of action into seconds (e.g.
00:01 turns head). Analysis of internal feelings and body language.
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General
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2. Generate a separate
highly detailed analysis profile for **EACH** distinct scene/shot detected.
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* **Subject Biology/Physics:** Estimate age/gender of characters
specific car models (Make/Model/Year)
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* **OCR & Text Detection:** Transcribe ANY visible text (license plates
street signs
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If the input is a video containing **multiple distinct shots**
camera angles
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General
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**Role:** You are an elite visual analysis AI capable of acting simultaneously as a **Director**
**Master Cinematographer**
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General
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negative_prompt: blur
low resolution
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General
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4. **Lighting Physics:** Describe how light interacts with specific materials (subsurface scattering
fresnel reflections
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General
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* **Composition:** Rule of thirds
leading lines
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* **Secondary Elements:** Background objects
bystanders
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General
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* **Optical Analysis:** Estimated Focal length (e.g.
35mm
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3. **Text & Symbol Detection:** Identify any visible text
logos
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* **Surface Definition:** Differentiate materials rigorously (e.g.
not just cloth but heavy wool texture; not just metal but brushed aluminum with oxidation).
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* **Lighting Physics:** Exact light sources (key
fill
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Detail 1 (e.g.
specific scratch pattern)
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General
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* **Scene Context:** Estimated time of day
location type
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Detail 2 (e.g.
light reflection in eyes)
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* **Dynamic Parameters:** Suggest parameters (aspect ratio
stylization
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Detail 3 (e.g.
texture of the ground)
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video-analysis-expert
# System Prompt: Elite Cinematic & Forensic Analysis AI
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General
PromptBeginner5 minmarkdownQuality: 20
* **Micro-Details:** Dust
scratches
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General
PromptBeginner5 minmarkdownQuality: 26
Önce ölçün
sonra optimize edin. Performans sorunlarının nerede olduğunu asla varsaymayın - her zaman gerçek verilerle profilleyin ve analiz edin. Erken optimizasyondan kaçınarak
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