Skill: guogithubname/nano-banana-pro-1-0-1 (version 1.0.0)

--- name: nano-banana-pro description: Generate/edit images with Nano Banana Pro (Gemini 3 Pro Image). Use for image create/modify requests incl. edits. Supports text-to-image + image-to-image; 1K/2K/4K; use --input-image. --- # Nano Banana Pro Image Generation & Editing Generate new images or edit existing ones using Google's Nano Banana Pro API (Gemini 3 Pro Image). ## Usage Run the script using absolute path (do NOT cd to skill directory first): **Generate new image:** ```bash uv run ~/.codex/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 ~/.codex/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] [--api-key KEY] ``` **Important:** Always run from the user's current working directory so images are saved where the user is working, not in the skill directory. ## Default Workflow (draft → iterate → final) Goal: fast iteration without burning time on 4K until the prompt is correct. - Draft (1K): quick feedback loop - `uv run ~/.codex/skills/nano-banana-pro/scripts/generate_image.py --prompt "<draft prompt>" --filename "yyyy-mm-dd-hh-mm-ss-draft.png" --resolution 1K` - Iterate: adjust prompt in small diffs; keep filename new per run - If editing: keep the same `--input-image` for every iteration until you’re happy. - Final (4K): only when prompt is locked - `uv run ~/.codex/skills/nano-banana-pro/scripts/generate_image.py --prompt "<final prompt>" --filename "yyyy-mm-dd-hh-mm-ss-final.png" --resolution 4K` ## Resolution Options The Gemini 3 Pro Image API supports three resolutions (uppercase K required): - **1K** (default) - ~1024px resolution - **2K** - ~2048px resolution - **4K** - ~4096px resolution Map user requests to API parameters: - No mention of resolution → `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 (use if user provided key in chat) 2. `GEMINI_API_KEY` environment variable If neither is available, the script exits with an error message. ## Preflight + Common Failures (fast fixes) - Preflight: - `command -v uv` (must exist) - `test -n \"$GEMINI_API_KEY\"` (or pass `--api-key`) - If editing: `test -f \"path/to/input.png\"` - Common failures: - `Error: No API key provided.` → set `GEMINI_API_KEY` or pass `--api-key` - `Error loading input image:` → wrong path / unreadable file; verify `--input-image` points to a real image - “quota/permission/403” style API errors → wrong key, no access, or quota exceeded; try a different key/account ## Filename Generation Generate filenames with the pattern: `yyyy-mm-dd-hh-mm-ss-name.png` **Format:** `{timestamp}-{descriptive-name}.png` - Timestamp: Current date/time in format `yyyy-mm-dd-hh-mm-ss` (24-hour format) - Name: Descriptive lowercase text with hyphens - Keep the descriptive part concise (1-5 words typically) - Use context from user's prompt or conversation - If unclear, use random identifier (e.g., `x9k2`, `a7b3`) Examples: - Prompt "A serene Japanese garden" → `2025-11-23-14-23-05-japanese-garden.png` - Prompt "sunset over mountains" → `2025-11-23-15-30-12-sunset-mountains.png` - Prompt "create an image of a robot" → `2025-11-23-16-45-33-robot.png` - Unclear context → `2025-11-23-17-12-48-x9k2.png` ## Image Editing When the user wants to modify an existing image: 1. Check if they provide an image path or reference an image in the current directory 2. Use `--input-image` parameter with the path to the image 3. The prompt should contain editing instructions (e.g., "make the sky more dramatic", "remove the person", "change to cartoon style") 4. Common editing tasks: add/remove elements, change style, adjust colors, blur background, etc. ## Prompt Handling **For generation:** Pass user's image description as-is to `--prompt`. Only rework if clearly insufficient. **For editing:** Pass editing instructions in `--prompt` (e.g., "add a rainbow in the sky", "make it look like a watercolor painting") Preserve user's creative intent in both cases. ## Prompt Templates (high hit-rate) Use templates when the user is vague or when edits must be precise. - Generation template: - “Create an image of: <subject>. Style: <style>. Composition: <camera/shot>. Lighting: <lighting>. Background: <background>. Color palette: <palette>. Avoid: <list>.” - Editing template (preserve everything else): - “Change ONLY: <single change>. Keep identical: subject, composition/crop, pose, lighting, color palette, background, text, and overall style. Do not add new objects. If text exists, keep it unchanged.” ## Output - Saves PNG to current directory (or specified path if filename includes directory) - Script outputs the full path to the generated image - **Do not read the image back** - just inform the user of the saved path ## Examples **Generate new image:** ```bash uv run ~/.codex/skills/nano-banana-pro/scripts/generate_image.py --prompt "A serene Japanese garden with cherry blossoms" --filename "2025-11-23-14-23-05-japanese-garden.png" --resolution 4K ``` **Edit existing image:** ```bash uv run ~/.codex/skills/nano-banana-pro/scripts/generate_image.py --prompt "make the sky more dramatic with storm clouds" --filename "2025-11-23-14-25-30-dramatic-sky.png" --input-image "original-photo.jpg" --resolution 2K ```

path: scripts/generate_image.py
content: #!/usr/bin/env python3
# /// script
# requires-python = ">=3.10"
# dependencies = [
#     "google-genai>=1.0.0",
#     "pillow>=10.0.0",
# ]
# ///
"""
Generate images using Google's Nano Banana Pro (Gemini 3 Pro Image) API.

Usage:
    uv run generate_image.py --prompt "your image description" --filename "output.png" [--resolution 1K|2K|4K] [--api-key KEY]
"""

import argparse
import os
import sys
from pathlib import Path


def get_api_key(provided_key: str | None) -> str | None:
    """Get API key from argument first, then environment."""
    if provided_key:
        return provided_key
    return os.environ.get("GEMINI_API_KEY")


def main():
    parser = argparse.ArgumentParser(
        description="Generate images using Nano Banana Pro (Gemini 3 Pro Image)"
    )
    parser.add_argument(
        "--prompt", "-p",
        required=True,
        help="Image description/prompt"
    )
    parser.add_argument(
        "--filename", "-f",
        required=True,
        help="Output filename (e.g., sunset-mountains.png)"
    )
    parser.add_argument(
        "--input-image", "-i",
        help="Optional input image path for editing/modification"
    )
    parser.add_argument(
        "--resolution", "-r",
        choices=["1K", "2K", "4K"],
        default="1K",
        help="Output resolution: 1K (default), 2K, or 4K"
    )
    parser.add_argument(
        "--api-key", "-k",
        help="Gemini API key (overrides GEMINI_API_KEY env var)"
    )

    args = parser.parse_args()

    # Get API key
    [REDACTED_SECRET])
    if not [REDACTED_SECRET]"Error: No API key provided.", file=sys.stderr)
        print("Please either:", file=sys.stderr)
        print("  1. Provide --api-key argument", file=sys.stderr)
        print("  2. Set GEMINI_API_KEY environment variable", file=sys.stderr)
        sys.exit(1)

    # Import here after checking API key to avoid slow import on error
    from google import genai
    from google.genai import types
    from PIL import Image as PILImage

    # Initialise client
    client = genai.Client([REDACTED_SECRET])

    # Set up output path
    output_path = Path(args.filename)
    output_path.parent.mkdir(parents=True, exist_ok=True)

    # Load input image if provided
    input_image = None
    output_resolution = args.resolution
    if args.input_image:
        try:
            input_image = PILImage.open(args.input_image)
            print(f"Loaded input image: {args.input_image}")

            # Auto-detect resolution if not explicitly set by user
            if args.resolution == "1K":  # Default value
                # Map input image size to resolution
                width, height = input_image.size
                max_dim = max(width, height)
                if max_dim >= 3000:
                    output_resolution = "4K"
                elif max_dim >= 1500:
                    output_resolution = "2K"
                else:
                    output_resolution = "1K"
                print(f"Auto-detected resolution: {output_resolution} (from input {width}x{height})")
        except Exception as e:
            print(f"Error loading input image: {e}", file=sys.stderr)
            sys.exit(1)

    # Build contents (image first if editing, prompt only if generating)
    if input_image:
        contents = [input_image, args.prompt]
        print(f"Editing image with resolution {output_resolution}...")
    else:
        contents = args.prompt
        print(f"Generating image with resolution {output_resolution}...")

    try:
        response = client.models.generate_content(
            model="gemini-3-pro-image-preview",
            contents=contents,
            config=types.GenerateContentConfig(
                response_modalities=["TEXT", "IMAGE"],
                image_config=types.ImageConfig(
                    image_size=output_resolution
                )
            )
        )

        # Process response and convert to PNG
        image_saved = False
        for part in response.parts:
            if part.text is not None:
                print(f"Model response: {part.text}")
            elif part.inline_data is not None:
                # Convert inline data to PIL Image and save as PNG
                from io import BytesIO

                # inline_data.data is already bytes, not base64
                image_data = part.inline_data.data
                if isinstance(image_data, str):
                    # If it's a string, it might be base64
                    import base64
                    image_data = base64.b64decode(image_data)

                image = PILImage.open(BytesIO(image_data))

                # Ensure RGB mode for PNG (convert RGBA to RGB with white background if needed)
                if image.mode == 'RGBA':
                    rgb_image = PILImage.new('RGB', image.size, (255, 255, 255))
                    rgb_image.paste(image, mask=image.split()[3])
                    rgb_image.save(str(output_path), 'PNG')
                elif image.mode == 'RGB':
                    image.save(str(output_path), 'PNG')
                else:
                    image.convert('RGB').save(str(output_path), 'PNG')
                image_saved = True

        if image_saved:
            full_path = output_path.resolve()
            print(f"\nImage saved: {full_path}")
        else:
            print("Error: No image was generated in the response.", file=sys.stderr)
            sys.exit(1)

    except Exception as e:
        print(f"Error generating image: {e}", file=sys.stderr)
        sys.exit(1)


if __name__ == "__main__":
    main()


path: _meta.json
content: ownerId: kn70pywhg0fyz996kpa8xj89s57yhv26
slug: nano-banana-pro
version: 1.0.1
publishedAt: 1767651987917

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)