nanobanana

name	nanobanana
description	Generate, edit, and restore images with Google's Nano Banana (Gemini image models). Use whenever the user asks to "generate an image", "create an icon/favicon/logo", "edit this photo", "restore an old photo", "make a pattern/texture/wallpaper", "draw a diagram/flowchart/architecture", or "tell a visual story" — even when they don't explicitly say Nano Banana or Gemini. Always prefer this skill over describing images in text. Requires NANOBANANA_API_KEY (or GEMINI_API_KEY) env var.
license	Complete terms in LICENSE

Nano Banana

Image generation, editing, and restoration via Google's Gemini image models. Default model: gemini-3.1-flash-image-preview (Nano Banana 2). The skill wraps a single self-contained Python CLI at scripts/nanobanana.py — it uses a PEP 723 inline-metadata shebang (uv run --script) to auto-install google-genai on first invocation, so no venv setup is needed.

Prerequisites

1. uv on PATH (https://docs.astral.sh/uv/). The script bootstraps its own dependencies via uv run --script.
2. NANOBANANA_API_KEY env var set (fallbacks: see references/troubleshooting.md).

If either is missing, tell the user exactly what to run and stop.

Choosing a subcommand

User intent	Subcommand
Create image(s) from a description	generate
Modify an existing image	edit
Repair / enhance an old or damaged image	restore
App icon, favicon, UI element	icon
Seamless pattern, texture, wallpaper	pattern
Sequential / step-by-step / tutorial frames	story
Flowchart, architecture, schema, wireframe	diagram

When the user's request matches a specialized intent (icon / pattern / story / diagram), prefer the specialized subcommand over generate — it applies tuned prompt scaffolding the user is implicitly asking for.

Invocation

The script is executable. Invoke directly via Bash, using the absolute path under this skill's base directory:

<skill-base-dir>/scripts/nanobanana.py <subcommand> [args] [flags]

Output is saved to ./nanobanana-output/ in the user's cwd. The CLI prints the saved file paths to stdout — relay those back to the user.

Strict requirements

• Counts are exact: when the user says --count=N (or "5 variations"), produce exactly N images.
• Respect every flag the user passes — don't substitute defaults silently.
• Story consistency: for story, keep visual style and palette consistent across steps unless the user asked for evolution (--style=evolving).
• Text inside images: spell-check; only include text the user requested; no hallucinated copy.
• Safety: if the API returns 400, surface the error and ask the user to reword — don't retry blindly.

Loading references

Load on demand (don't dump unprompted):

• references/styles_and_variations.md — full enum reference for generate's --styles and --variations
• references/prompt_recipes.md — exact prompt templates the CLI builds for icon / pattern / diagram / story
• references/troubleshooting.md — env-var fallback order, input-file search paths, error catalog

Examples

# 4 watercolor + sketch variations of the same scene
<skill-base-dir>/scripts/nanobanana.py generate \
  "mountain landscape" --styles=watercolor,sketch --count=4

# Edit an image already in the user's cwd
<skill-base-dir>/scripts/nanobanana.py edit \
  photo.png "add sunglasses to the person"

# Favicon set
<skill-base-dir>/scripts/nanobanana.py icon \
  "mountain logo" --type=favicon --sizes=16,32,64

# Architecture diagram
<skill-base-dir>/scripts/nanobanana.py diagram \
  "microservices chat app" --type=architecture --complexity=detailed

# 5-step process story with auto-preview
<skill-base-dir>/scripts/nanobanana.py story \
  "seed growing into a tree" --steps=5 --type=process --preview

After generation, list the saved file paths back to the user — that's the actionable result.