nano-banana

# Nano Banana Skill

Python scripting with Gemini image generation using uv. Write small, focused scripts using heredocs for quick tasks—no files needed for one-off operations.

## Choosing Your Approach

**Quick image generation**: Use heredoc with inline Python for one-off image requests.

**Complex workflows**: When multiple steps are needed (generate -> refine -> save), break into separate scripts and iterate.

**Scripting tasks**: For non-image Python tasks, use the same heredoc pattern with `uv run`.

## Writing Scripts

Execute Python inline using heredocs with inline script metadata for dependencies:

```bash
uv run - << 'EOF'
# /// script
# dependencies = ["google-genai", "pillow"]
# ///
from google import genai
from google.genai import types

client = genai.Client()

response = client.models.generate_content(
    model="gemini-2.5-flash-image",
    contents=["A cute banana character with sunglasses"],
    config=types.GenerateContentConfig(
        response_modalities=['IMAGE']
    )
)

for part in response.parts:
    if part.inline_data is not None:
        image = part.as_image()
        image.save("tmp/generated.png")
        print("Image saved to tmp/generated.png")
EOF
```

The `# /// script` block declares dependencies inline using TOML syntax. This makes scripts self-contained and reproducible.

**Why these dependencies:**
- `google-genai` - Gemini API client
- `pillow` - Required for `.as_image()` method (converts base64 to PIL Image) and saving images

**Only write to files when:**
- The script needs to be reused multiple times
- The script is complex and requires iteration
- The user explicitly asks for a saved script

### Basic Template

```bash
uv run - << 'EOF'
# /// script
# dependencies = ["google-genai", "pillow"]
# ///
from google import genai
from google.genai import types

client = genai.Client()

# Generate image
response = client.models.generate_content(
    model="gemini-2.5-flash-image",
    contents=["YOUR PROMPT HERE"],
    config=