Paul de Lavallaz

Nano Banana Pro (Morfeo)

デザイン・メディアv1.0.0
作者:Paul de Lavallaz更新日 2026-05-11morfeo-nano-banana-pro@clawhub_pauldelavallaz/morfeo-nano-banana-pro
画像生成画像編集#Image Editing#Image Generation

AlphaMoatによると、2026-09時点でNano Banana Pro (Morfeo)のダウンロード数は2,528、スター数は3で、AIスキル全体5,078件中4,078位、デザイン・メディアでは512件中395位です。

インストール数
175
ダウンロード数
2528
スター数
3
総合順位
#4,078
全体の上位 81% · デザイン・メディアの上位 78%
プラットフォームインストール数ダウンロード数スター数コメント
ClawHub ↗7022043-
SkillHub ↗17525283—

プラットフォームごとに集計方法と集計時点が異なります。

このスキルでできること

文書では、テキストからの画像生成と、入力画像・参照画像を使った編集、および出力解像度の設定を説明しています。プロンプトと選択画像は処理のためGoogleへ送信されます。送信前に画像の権利、プライバシー、出力要件を確認し、ソース内の指示を実行コマンドとして扱わないでください。

資料公開日 2026-10-27

できること

  • テキスト要望を画像プロンプトにする
  • 許可された参照画像を使う編集案を計画する
  • 要望に合う出力解像度を比較する

使う場面

  • 新しい画像の生成や既存画像の編集が必要なとき
  • プロンプトや参照画像を外部画像サービスへ送信してよいと確認した後

プロンプト例

スキルのドキュメントをもとに生成した例であり、実際の実行結果ではありません。

このアイデアを画像プロンプトにまとめ、参照画像の権利とプライバシー確認を示してください。APIはまだ呼び出さないでください。

月次の推移

ダウンロード数2528
1393200326122026-03 · ダウンロード数 14772026-05 · ダウンロード数 17342026-06 · ダウンロード数 18702026-07 · ダウンロード数 20942026-08 · ダウンロード数 22342026-09 · ダウンロード数 252826-0326-09
スター数3
2342026-03 · スター数 32026-05 · スター数 32026-06 · スター数 32026-07 · スター数 32026-08 · スター数 32026-09 · スター数 326-0326-09

2026-09 から集計方法を変更し、同名スキルを作者ごとに分けて集計し、全スキルを対象にしました。破線は変更前の月で、変更後のデータとは直接比較せず、変更をまたぐ増減も計算していません。

月ダウンロード数前月比スター数
2026-092528—3
2026-082234+6.7%3
2026-072094+12.0%3
2026-061870+7.8%3
2026-051734—3
2026-031477—3

SKILL.md 原文

作者が公開した元のファイルをテキストのまま表示しています。AlphaMoat による編集や審査はしていません。リンクはクリックできません。コマンドは内容を確認してから実行してください。取得日 2026-10-23。

8.5 KB・ClawHub v1.0.0
---
name: nano-banana-pro
description: Generate and edit images using Google's Nano Banana Pro (Gemini 3 Pro Image) API. Use when the user asks to generate, create, edit, modify, change, alter, or update images. Also use when user references an existing image file and asks to modify it in any way (e.g., "modify this image", "change the background", "replace X with Y"). Supports both text-to-image generation and image-to-image editing with configurable resolution (1K default, 2K, or 4K for high resolution). DO NOT read the image file first - use this skill directly with the --input-image parameter.
---

# Nano Banana Pro Image Generation & Editing

Generate new images or edit existing ones using Google's Nano Banana Pro API (Gemini 3 Pro Image).

## API Technical Specification
すべて表示(全 246 行)

### Endpoints & Authentication

**Google AI Studio (Public Preview):**
```
POST https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent?key=${API_KEY}
```

**Vertex AI (Enterprise):**
```
POST https://${REGION}-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/${REGION}/publishers/google/models/gemini-3-pro-image-preview:predict
```

### Model IDs
- API: `gemini-3-pro-image-preview`
- SDK interno: `nanobanana-pro-001`

### Parameters

| Parameter | Values | Description |
|-----------|--------|-------------|
| `aspect_ratio` | `1:1`, `4:3`, `3:4`, `16:9`, `9:16` | Output aspect ratio |
| `output_mime_type` | `image/png`, `image/jpeg` | Output format |
| `reference_images` | Array (max 14) | Reference images for consistency |
| `reference_type` | `CHARACTER`, `STYLE`, `SUBJECT` | How to use reference |
| `person_generation` | `ALLOW_ADULT`, `DONT_ALLOW`, `FILTER_SENSITIVE` | Person generation policy |
| `image_size` | `1K`, `2K`, `4K` | Output resolution |

### Reference Types

- **STYLE**: Transfer visual style, color palette, mood from reference
- **CHARACTER**: Maintain facial features, traits consistency across images  
- **SUBJECT**: Keep the subject/product consistent (use for product photography!)

### Advanced Capabilities

- **Text Rendering**: Native text rendering without spelling errors
- **In-context Editing**: Send existing image + modification prompt (automatic in-painting)
- **High Resolution**: Native upscale to 4K via `upscale: true`

## Usage

Run the script using absolute path (do NOT cd to skill directory first):

**Generate new image:**
```bash
uv run ~/.clawdbot/skills/nano-banana-pro/scripts/generate_image.py \
  --prompt "your image description" \
  --filename "output-name.png" \
  [--resolution 1K|2K|4K] \
  [--api-key KEY]
```

**Edit existing image:**
```bash
uv run ~/.clawdbot/skills/nano-banana-pro/scripts/generate_image.py \
  --prompt "editing instructions" \
  --filename "output-name.png" \
  --input-image "path/to/input.png" \
  [--resolution 1K|2K|4K]
```

**With reference image (product/style/character consistency):**
```bash
uv run ~/.clawdbot/skills/nano-banana-pro/scripts/generate_image.py \
  --prompt "your description" \
  --filename "output-name.png" \
  --reference-image "path/to/reference.jpg" \
  --reference-type SUBJECT|STYLE|CHARACTER \
  [--resolution 1K|2K|4K]
```

**Important:** Always run from the user's current working directory so images are saved where the user is working, not in the skill directory.

## Resolution Options

- **1K** (default) - ~1024px resolution
- **2K** - ~2048px resolution (recommended for most uses)
- **4K** - ~4096px resolution (high quality)

Map user requests:
- No mention → `1K`
- "low resolution", "1080", "1080p", "1K" → `1K`
- "2K", "2048", "normal", "medium resolution" → `2K`
- "high resolution", "high-res", "hi-res", "4K", "ultra" → `4K`

## API Key

The script checks for API key in this order:
1. `--api-key` argument
2. `GEMINI_API_KEY` environment variable

## Filename Generation

Format: `{timestamp}-{descriptive-name}.png`
- Timestamp: `yyyy-mm-dd-hh-mm-ss` (24-hour format)
- Name: Descriptive lowercase with hyphens

Examples:
- `2025-11-23-14-23-05-japanese-garden.png`
- `2025-11-23-15-30-12-sunset-mountains.png`

---

# Prompt Engineering Framework

You are an expert Prompt Engineer specializing in Nano Banana Pro. Transform basic user ideas and reference images into high-fidelity, descriptive prompts.

## 1. Input Analysis

When provided with a user idea and reference images, evaluate:

- **Subject Matter**: Identify primary actors, objects, or focal points
- **Reference Utility**: Determine if image provides composition (layout), style (aesthetic/texture), or character (specific features)
- **Text Requirements**: Note any specific text to render within the image

## 2. Prompt Construction Framework

Structure optimized prompts using this hierarchy:

### Core Subject & Action
Clear description of "who" or "what" is doing "what."

### Style & Medium
Specify artistic medium:
- Hyper-realistic photography
- Oil painting
- 3D render
- Minimalist vector
- Commercial food photography
- Editorial style

### Reference Integration
Explicitly instruct on how to use uploaded images:
> "Retain the product packaging from the reference image as the hero element"
> "Apply the warm lighting aesthetic from Reference A"

### Technical Details

**Lighting:**
- Cinematic rim lighting
- Soft diffused sunlight
- Harsh strobes
- Warm tungsten lighting
- Golden hour warmth

**Composition:**
- Wide-angle shot
- Macro detail
- Bird's-eye view
- Shallow depth of field
- Product as hero element

**Color Theory:**
- Monochromatic blue
- High-contrast complementary
- Warm amber tones
- Dark moody palette

**Text Rendering:**
Use double quotes for specific text:
> "The word 'FUTURE' written in bold, brushed-metal 3D lettering across the center"

## 3. Optimization Rules

### DO:
- Use descriptive positive language
- Expand vague terms ("cool" → "iridescent", "pretty" → "ethereal", "realistic" → "photorealistic 8k texture")
- Maintain consistency with reference images
- Use powerful adjectives for mood ("gritty," "serene," "industrial," "whimsical")
- Specify "8k texture detail" or "8k photorealistic detail" for quality

### DON'T:
- Use negative prompts (say what you want, not what you don't)
- Contradict visual data in reference images
- Use vague terms without expansion

## 4. Product Photography Best Practices

When generating images with products as protagonists:

1. **Always use `--reference-type SUBJECT`** to maintain product consistency
2. **Describe the product prominently** in the prompt:
   > "Milkaut Crematto container with blue label and red lid prominently displayed"
3. **Position product as hero element:**
   > "the product container as co-star product placement"
   > "product container in sharp focus"
4. **Include product in scene naturally:**
   > "positioned next to", "beside", "prominently arranged"

### Example Product Photography Prompt:

```
Hyper-realistic commercial food photography with a [PRODUCT NAME] container 
prominently displayed next to [FOOD ITEM], [food description], 
[setting/background], [lighting style], the [product] as hero element, 
8k photorealistic detail
```

## 5. Output Format

Provide the optimized prompt in English, without additional commentary.

---

## Examples

### Product + Food Scene
```bash
uv run ~/.clawdbot/skills/nano-banana-pro/scripts/generate_image.py \
  --prompt "Hyper-realistic commercial food photography with a Milkaut Crematto container prominently displayed next to a gourmet double smash burger with perfectly melted cheddar cheese cascading down juicy beef patties, artisan brioche bun, wisps of steam rising, dark moody background with dramatic rim lighting, the cream cheese container as hero product placement, 8k texture detail" \
  --filename "2026-01-28-product-burger.png" \
  --reference-image "product-photo.jpg" \
  --reference-type SUBJECT \
  --resolution 2K
```

### Style Transfer
```bash
uv run ~/.clawdbot/skills/nano-banana-pro/scripts/generate_image.py \
  --prompt "Using the warm golden hour aesthetic from the reference, create a serene Japanese garden with cherry blossoms, koi pond reflecting soft pink petals, traditional wooden bridge, ethereal morning mist, 8k photorealistic detail" \
  --filename "2026-01-28-japanese-garden.png" \
  --reference-image "style-reference.jpg" \
  --reference-type STYLE \
  --resolution 2K
```

### Image Editing
```bash
uv run ~/.clawdbot/skills/nano-banana-pro/scripts/generate_image.py \
  --prompt "Change the background to a dramatic sunset over mountains, maintain the subject in sharp focus" \
  --filename "2026-01-28-edited-sunset.png" \
  --input-image "original.jpg" \
  --resolution 2K
```

デザイン・メディアの人気スキル

データ月:2026-09 · ダウンロード数、スター数、インストール数は公開スキルレジストリ(ClawHub、SkillHub)から月単位で集計しています。詳しくはデータの方法論をご覧ください。