image

分类: 数据与AI | 上传者: smixs | 下载: 0 | 版本: v1.0（最新）

Nano Banana (NBP/NB2) 和 GPT Image 2 的图像提示技能。编写带有型号/质量/尺寸建议的即用型提示。使用场合：“нарисуй”、“сгенерируй картинку”、“图像提示”、“промпт для картинки”、博客封面、幻灯片、海报、产品照片、UI 模型、故事板、角色表、编辑/着色、风格转换、视觉分析、图像提示、NB、NBP、NB2、 gpt-image-2、多面板网格、电子商务产品摄影、时尚社论、食品/饮料广告、电影肖像。

更新日志: Source: GitHub https://github.com/smixs/visual-skills

目录结构

当前层级: tree/main/image/

📁 references/
- 📁 patterns/
  - 📄 character-design.md 12.1 KB
  - 📄 ecommerce.md 9.5 KB
  - 📄 fashion-editorial.md 10.7 KB
  - 📄 food-beverage.md 11.4 KB
  - 📄 portrait-cinema.md 12.4 KB
  - 📄 poster-illustration.md 15.0 KB
  - 📄 ui-social.md 13.8 KB
- 📄 characters.md 2.0 KB
- 📄 creative-direction.md 4.5 KB
- 📄 dimensional.md 1.9 KB
- 📄 editing.md 2.6 KB
- 📄 golden-rules.md 8.6 KB
- 📄 gpt-image.md 8.3 KB
- 📄 models.md 5.5 KB
- 📄 multi-panel.md 15.8 KB
- 📄 nano-banana.md 4.3 KB
- 📄 prompt-framework.md 13.0 KB
- 📄 slides.md 9.7 KB
- 📄 storyboards.md 2.1 KB
- 📄 structural.md 1.9 KB
- 📄 text-rendering.md 5.4 KB
- 📄 vision-decomposer.md 6.4 KB
📄 SKILL.md 6.9 KB

SKILL.md

---
name: image
description: >
  Image prompting skill for Nano Banana (NBP/NB2) and GPT Image 2. Writes ready-to-use
  prompts with model/quality/size recommendations. Use when: "нарисуй", "сгенерируй
  картинку", "image prompt", "промпт для картинки", blog covers, slides, posters,
  product shots, UI mockups, storyboards, character sheets, edit/colorize, style transfer,
  vision analysis, image-to-prompt, nb, NBP, NB2, gpt-image-2, multi-panel grids,
  ecommerce product photography, fashion editorial, food/beverage ads, cinematic portraits.
  Do NOT use for: video (use video skill), 3D models, audio, non-image tasks.
---

# Image Prompting — Nano Banana & GPT Image 2

This skill writes image prompts. It does not generate images. The output is: model name + quality / size / aspect ratio + the prompt itself.

The body of this SKILL.md is intentionally thin so you cannot fake a result by reading it alone. The actual rules — what the models reward, what they punish, how to phrase a 5-slot template, when to add `quality: high`, when to use image grounding — live only in the reference files.

---

# Mandatory reading order — DO NOT WRITE A PROMPT WITHOUT THIS

Past attempts to write prompts directly from this skill body produced lazy, generic results. Each model has its own physics; common rules collapse into mush when applied without model-specific syntax. Read in this order before producing any prompt:

### Step 1 — always read first → [models.md](references/models.md)

Decide: Nano Banana (NB2 or NBP) or GPT Image 2. The choice changes the prompt syntax fundamentally — natural-language paragraphs vs. labeled 5-slot template, quality settings, which features exist (image grounding only on NB, EXACT TEXT discipline only on GPT Image, etc.).

If the user named a model — confirm and proceed. If not — pick using the table in `models.md`, then state your choice in the output header.

### Step 2 — read **one** model file (the one you picked)

- **Nano Banana** → [nano-banana.md](references/nano-banana.md)
  Image grounding for real locations. Extreme aspect ratios (1:8, 8:1, 4:1). Thinking mode. JSON for 5+ elements. Up to 14 reference images. Why you must NOT write `50mm / f-stop / ISO` numbers.

- **GPT Image 2** → [gpt-image.md](references/gpt-image.md)
  5-slot template (Scene / Subject / Important Details / Use Case / Constraints). Anti-slop banned-words list. `quality: low / medium / high` as a deliberate fidelity lever. Size constraints (multiples of 16, max 3:1, up to 2560×1440). Two-column edit logic (Change / Preserve / Constraints). Up to 16 reference images with explicit roles.

The model file is non-negotiable. Skipping it is the single biggest cause of weak prompts.

### Step 3 — always read after the model file → [golden-rules.md](references/golden-rules.md)

Universal rules that apply to both models: start with a verb, positive framing, hex colors, quote text, edit don't re-roll, one change per iteration, reference images.

### Step 4 — task-shaped reading (load only what matches the request)

Pick zero or more, depending on what the user asked for:

- Text in image, infographic, diagram, multilingual rendering → [text-rendering.md](references/text-rendering.md)
- Edit existing image (object removal, lighting swap, colorization, restoration, localization) → [editing.md](references/editing.md)
- Character continuity across multiple images / panels → [characters.md](references/characters.md)
- Presentation slides → [slides.md](references/slides.md)
- Sequential narrative (storyboard, comic, panel sequence) → [storyboards.md](references/storyboards.md)
- Sketch → final, wireframes, structural input → [structural.md](references/structural.md)
- 2D → 3D, floor plans, isometric → [dimensional.md](references/dimensional.md)
- **Vision analysis / image-to-prompt / style transfer from a reference image** → [vision-decomposer.md](references/vision-decomposer.md). Load this whenever the user attaches an image and asks to recreate, match, decompose, or transfer its style.
- **Multi-panel compositions** (grids, collages, storyboard sheets in ONE image) → [multi-panel.md](references/multi-panel.md). 9-cell TVC grids, 2x2 portrait grids, 3-panel campaign collages, 4x3 borderless grids, 6-frame cinematic sequences, before/after splits, 12-panel storyboard posters.
- **Industry pattern libraries** — proven prompt templates by vertical. Load the matching file:
  - E-commerce product shots → [patterns/ecommerce.md](references/patterns/ecommerce.md)
  - Fashion editorial campaigns → [patterns/fashion-editorial.md](references/patterns/fashion-editorial.md)
  - Food & beverage advertising → [patterns/food-beverage.md](references/patterns/food-beverage.md)
  - Cinematic portraits → [patterns/portrait-cinema.md](references/patterns/portrait-cinema.md)
  - Posters & illustration → [patterns/poster-illustration.md](references/patterns/poster-illustration.md)
  - Character design (turnarounds, expression sheets, outfit grids) → [patterns/character-design.md](references/patterns/character-design.md)
  - UI mockups & social media formats → [patterns/ui-social.md](references/patterns/ui-social.md)

### Step 5 — read for production language → [creative-direction.md](references/creative-direction.md)

Studio-quality vocabulary for lighting design, camera and hardware, color grading and film stock, materiality and texture. Read when you need precise terms beyond what `golden-rules.md` covers.

### Step 6 — read if structuring a complex prompt → [prompt-framework.md](references/prompt-framework.md)

Universal element checklist (subject, context, action, environment, camera, lighting, mood, materials, palette, format), detail modes (concise / standard / verbose / cinematic verbose), parameterized templates, output structure with parameters and exclusions.

---

# Output format

When you return the prompt, structure it like this:

```
Model: <nano-banana-2 | nano-banana-pro | gpt-image-2>
Quality: <low | medium | high>          (only for gpt-image-2)
Size / Ratio: <e.g. 1536×1024 or 16:9>

Prompt:
<the prompt text, ready to copy>

Notes:
- <anything you inferred or assumed because the user did not specify>
```

For edits, also include an explicit preserve-list (mandatory for gpt-image-2, recommended for nano-banana):

```
Change: <one concrete thing>
Preserve: <face, pose, lighting, framing, geometry, ...>
Constraints: <no extra objects, no drift, ...>
```

---

# Final response style

Prefer: ready-to-copy prompts, hex colors, concrete materials, named compositions, model-specific syntax (5-slot for GPT Image, natural prose for Nano Banana).

Avoid: tag soup ("cool, modern, 4k"), vague praise ("stunning, epic, masterpiece" — actively hurts GPT Image 2), negative framing ("no people, no cars" — invert to positive), external comparisons ("like Apple ad" — describe the visual properties instead), numerical lens parameters in Nano Banana prompts (it ignores them).

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