Find agent skills by outcome

- Scikit-learn (or light ML

no heavy black-box models initially)

You may propose alternatives

but default to:

- Turkey-specific market dynamics (inflation

taxes

At minimum

support the following inputs:

- Fuel type (Petrol

Diesel

- Prefer robust estimators (median

trimmed mean

- Manipulation

emotional pricing

You will work in an agent-style

vibe coding approach:

- Resources: Identify and prepare texts

videos

- Develop a clear plot structure with a beginning

middle

- Execution: Detail how the workshop will be conducted (online

virtual

Education

- Evaluation: Explain how the workshop will be evaluated

mentioning options like journals

- Existing marketplaces (e.g.

classified ad platforms) have highly volatile

You are building a full-stack web and mobile application inspired by the Kelley Blue Book – What's My Car Worth? concept

but strictly tailored for the Turkish automotive market.

angle: Eye-level

orthographic feel

shadows: Minimal

soft-edged

style_goal: Referans görseldeki kişiyi/konuyu

aynı kompozisyonu koruyarak yüksek kontrastlı neon-ink poster illüstrasyonu stiline dönüştür.

Car poster

${primary_text:Megane}{

style: Clean motorsport typography

realistic vinyl application

Marka

model

main: Dikey (9:16) sinematik portre illüstrasyonu: referans görseldeki ana konu (kişi/figür) aynı poz ve kadrajda kalsın. Stil: koyu lacivert/siyah mürekkep dokuları ve kalın konturlar; yüz ve kıyafet üzerinde oyma/gravür benzeri ince çizgisel gölgelendirme (etched shading)

cel-shading ile birleşen poster estetiği. Arka plan: düz

focal_length_equivalent: 70-100mm (compressed

distortion-free)

emotion: Precision

speed

fidelity: High material accuracy (paint

carbon fiber