corespeed-nanobanana

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name: corespeed-nanobanana description: Generate and edit images using Google Gemini models via Corespeed AI Gateway. Supports text-to-image generation, image editing, multi-image input, and text rendering in images using gemini-2.5-flash-image. Also supports text generation and image analysis with gemini-2.5-flash, gemini-2.5-pro, and gemini-2.5-flash-lite. Use when a user asks to create images, edit photos, analyze images, or generate text with Gemini models. metadata: { "openclaw": { "emoji": "🍌", "requires": { "bins": ["uv"], "env": ["CS_AI_GATEWAY_BASE_URL", "CS_AI_GATEWAY_API_TOKEN"] }, "install": [ { "id": "uv-pip", "kind": "shell", "command": "pip install uv || pip3 install uv", "bins": ["uv"], "label": "Install uv via pip (https://github.com/astral-sh/uv)", }, ], }, }

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Corespeed NanoBanana — Gemini Image & Text Generation

Auth: Set CS_AI_GATEWAY_BASE_URL and CS_AI_GATEWAY_API_TOKEN environment variables.

Workflow

1. Pick a model from the table below (default: gemini-2.5-flash-image for image generation) 2. Run the script with your prompt

Usage

uv run {baseDir}/scripts/gemini.py --prompt "your prompt" -f output.ext [-i input.ext] [--model MODEL]

--prompt, -p — Text prompt (required)

--filename, -f — Output filename (required)

--input, -i — Input image file(s), repeat for multiple

--model, -m — Model name (default: gemini-2.5-flash-image)

--modalities — Response type: auto, image, text, image+text (default: auto)

--json — Output structured JSON (recommended for agent consumption)

Output format is determined by file extension: .png/.jpg → image generation, .txt/.md → text output.

Image Generation

# Text-to-image
uv run {baseDir}/scripts/gemini.py -p "a watercolor fox in autumn forest" -f fox.png
Image editing
uv run {baseDir}/scripts/gemini.py -p "Remove background, add beach sunset" -f edited.png -i photo.jpg
Multi-image compositing
uv run {baseDir}/scripts/gemini.py -p "Blend these two scenes together" -f blend.png -i scene1.png -i scene2.png

Image Analysis

# Describe an image
uv run {baseDir}/scripts/gemini.py -p "Describe this image" -f desc.txt -i photo.jpg --model gemini-2.5-flash
Compare images
uv run {baseDir}/scripts/gemini.py -p "What are the differences?" -f diff.txt -i before.jpg -i after.jpg --model gemini-2.5-flash

Text Generation

# Use the most capable model for complex tasks
uv run {baseDir}/scripts/gemini.py -p "Write a haiku about coding" -f haiku.txt --model gemini-2.5-pro

Models

| Model | Type | Best For | |-------|------|----------| | gemini-2.5-flash-image | Image + Text | Image generation & editing (default) | | gemini-2.5-flash | Text | Fast analysis, vision, general tasks | | gemini-2.5-pro | Text | Complex reasoning, highest quality | | gemini-2.5-flash-lite | Text | Fastest, simple tasks |

Notes

No manual Python setup required. The script uses PEP 723 inline metadata. uv run automatically creates an isolated virtual environment and installs the google-genai dependency on first run.

Image output is returned inline as base64 from the Gemini API — no separate download step.

Use timestamps in filenames: yyyy-mm-dd-hh-mm-ss-name.ext.

Script prints MEDIA: line for OpenClaw to auto-attach generated images.

Do not read generated media back; report the saved path only.

Only gemini-2.5-flash-image can generate images. Other models are text-only.

Use --json for structured output: {"ok": true, "files": [...], "text": "...", "model": "...", "tokens": {...}}

Support

Built by Corespeed. If you need help or run into issues:

💬 Discord: discord.gg/mAfhakVRnJ

🐦 X/Twitter: @CoreSpeed_io

🐙 GitHub: github.com/corespeed-io/skills

uv run {baseDir}/scripts/gemini.py --prompt "your prompt" -f output.ext [-i input.ext] [--model MODEL]

--prompt, -p — Text prompt (required)

--filename, -f — Output filename (required)

--input, -i — Input image file(s), repeat for multiple

--model, -m — Model name (default: gemini-2.5-flash-image)

--modalities — Response type: auto, image, text, image+text (default: auto)

--json — Output structured JSON (recommended for agent consumption)

Output format is determined by file extension: .png/.jpg → image generation, .txt/.md → text output.

No manual Python setup required. The script uses PEP 723 inline metadata. uv run automatically creates an isolated virtual environment and installs the google-genai dependency on first run.

Image output is returned inline as base64 from the Gemini API — no separate download step.

Use timestamps in filenames: yyyy-mm-dd-hh-mm-ss-name.ext.

Script prints MEDIA: line for OpenClaw to auto-attach generated images.

Do not read generated media back; report the saved path only.

Only gemini-2.5-flash-image can generate images. Other models are text-only.

Use --json for structured output: {"ok": true, "files": [...], "text": "...", "model": "...", "tokens": {...}}