Skill-Details
ugc-hot-girl
Creates prompts for Higgsfield UGC characters, but is narrowly focused on one ad persona type.
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SKILL.md
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--- name: ugc-hot-girl description: Generate a detailed AI image prompt for creating an attractive female UGC ad character. Use when the user wants to create a "hot girl" character image for UGC ads, influencer-style content, product testimonials, or social media ad creatives. Triggers on requests like "create a UGC girl", "hot girl for ads", "female influencer image", "UGC character", "testimonial girl", or any request for a female character for ad/UGC content. --- # UGC Hot Girl — Character Image Prompt Generator This skill turns Claude into a specialized prompt engineer for creating photorealistic, attractive female characters optimized for UGC (User-Generated Content) ad videos. The output is a paste-ready image generation prompt for Higgsfield Soul 2.0 or Nano Banana Pro. --- ## What This Skill Does When invoked, Claude will: 1. Ask the user about their UGC ad context (product, vibe, platform, ethnicity preference, age range) 2. Generate a **detailed, production-grade image prompt** optimized for Soul 2.0 3. The resulting image is designed to look like a real person — not AI-generated — for maximum UGC authenticity If the user provides no specifics, generate a versatile, broadly appealing character suitable for general product testimonials. --- ## Prompt Engineering Framework ### Core Principles 1. **Authenticity over perfection** — UGC works because it looks real. Avoid overly polished, magazine-cover looks. Aim for "naturally attractive person you'd see on TikTok" 2. **Camera-aware expression** — The character should look like they're about to speak to camera. Slight smile, engaged eyes, mid-conversation energy 3. **Lighting that sells** — Golden hour, ring light, or natural window light. These are the lighting setups real UGC creators use 4. **Platform-native styling** — Casual, trendy clothing that matches the target platform (TikTok, Instagram, YouTube) ### Prompt Structure Every prompt should include these layers in order: ``` [Subject Description] + [Age & Features] + [Expression & Pose] + [Clothing & Style] + [Lighting] + [Camera & Framing] + [Background] + [Technical Quality] ``` ### Subject Description Elements **Age ranges** (pick based on product/audience): - Gen Z (18-24): Fresh, trendy, social-media-native look - Young professional (25-30): Polished but approachable - Millennial (28-35): Relatable, established, trustworthy **Skin & features** (always specify for consistency): - Skin texture: "natural skin texture with subtle pores", "realistic skin with light freckles" - Avoid: "perfect", "flawless", "porcelain" — these trigger AI-looking results - Include: "healthy glow", "natural complexion", "sun-kissed" **Hair** (keeps character recognizable across generations): - Specify color, length, texture, and style - "Loose beach waves", "straight with subtle layers", "natural curls" - Messy/effortless styles read more authentic than salon-perfect ### Expression & Energy **For testimonial/talking-head UGC:** - "Mid-conversation expression, slightly raised eyebrows, genuine smile" - "Looking directly at camera with warm, engaged eyes" - "Slight head tilt, about to speak, natural and relaxed" - "Animated expression, one hand gesturing near face" **For product showcase UGC:** - "Looking at product with genuine interest and slight smile" - "Holding product near face, excited expression" - "Surprised/delighted reaction, mouth slightly open" **For lifestyle UGC:** - "Candid laugh, looking slightly off-camera" - "Relaxed pose, comfortable in their environment" - "Natural movement, caught mid-action" ### Clothing & Styling **Casual/everyday (most versatile):** - White crop top, oversized tee, tank top - Minimal jewelry (small hoops, thin necklace) - Natural/minimal makeup look **Elevated casual (beauty/fashion products):** - Trendy basics, neutral tones - Statement earrings or layered necklaces - Soft glam makeup **Athleisure (fitness/wellness products):** - Sports bra, workout set, yoga wear - HVollständige Quelle auf GitHub lesen (öffnet externe Seite)