Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add cnemri/google-genai-skills --skill nano-banana-buildgit clone --depth 1 https://github.com/cnemri/google-genai-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/cnemri/google-genai-skills/nano-banana-build)<a href="https://agentmods.dev/skills/cnemri/google-genai-skills/nano-banana-build"><img src="https://agentmods.dev/badge/skills/cnemri/google-genai-skills/nano-banana-build/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cnemri/google-genai-skills/nano-banana-build"><img src="https://agentmods.dev/badge/skills/cnemri/google-genai-skills/nano-banana-build.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00047 | $0.00503 |
| Opus 5.5 | $0.00019 | $0.00201 |
| Sonnet 5.5 | $0.00009 | $0.00101 |
| Haiku 4.5 | $0.00005 | $0.00050 |
Grade A, and why
nano-banana-build scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Nano Banana Image Generation Skill
Use this skill to generate and edit images using the google-genai Python SDK with Gemini's specialized image models (Nano Banana).
Quick Start Setup
from google import genai
from google.genai import types
from PIL import Image
import io
client = genai.Client()
Reference Materials
- Model Capabilities: Comparison of Gemini 2.5 vs 3 Pro, resolutions, and token costs.
- Image Generation: Text-to-Image, Interleaved Text/Image.
- Image Editing: Subject Customization, Style Transfer, Multi-turn Editing.
- Thinking Process: Understanding thoughts and signatures (Gemini 3 Pro).
- Recipes: Extensive collection of examples (Logos, Stickers, Mockups, Comics, etc.).
- Source Code: Deep inspection of SDK internals.
Available Models
gemini-2.5-flash-image(Nano Banana): Fast, high-quality generation and editing. Best for most use cases.gemini-3-pro-image-preview(Nano Banana Pro): Highest fidelity, supports2Kand4Kresolution, complex prompt adherence, and grounding.
Common Workflows
1. Fast Generation
response = client.models.generate_content(
model='gemini-2.5-flash-image',
contents='A cute robot eating a banana',
config=types.GenerateContentConfig(
response_modalities=['IMAGE']
)
)
2. High-Quality Editing
response = client.models.generate_content(
model='gemini-3-pro-image-preview',
contents=[
types.Part.from_uri(file_uri='gs://.../shoe.jpg', mime_type='image/jpeg'),
"Change the color of the shoe to neon green."
],
config=types.GenerateContentConfig(response_modalities=['IMAGE'])
)
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 59 lines · 47 tokens per session scan A e3044cc3b9cc
nano-banana-build is a skill published in the GitHub repository cnemri/google-genai-skills (129 stars, last pushed 9mo ago), licensed MIT. It adds 47 tokens to every session and 503 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
nano-banana-build compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| nano-banana-buildcnemri/google-genai-skills | Skill | 129 | 9mo ago | MIT | A | 47 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |