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 Orkas-AI/Orkas-Awesome-AgentSkills --skill nano-bananagit clone --depth 1 https://github.com/Orkas-AI/Orkas-Awesome-AgentSkillsWrote 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/orkas-ai/orkas-awesome-agentskills/nano-banana)<a href="https://agentmods.dev/skills/orkas-ai/orkas-awesome-agentskills/nano-banana"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas-awesome-agentskills/nano-banana/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/orkas-ai/orkas-awesome-agentskills/nano-banana"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas-awesome-agentskills/nano-banana.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 | $0.00003 | $0.00795 |
| Opus 5 | $0.00002 | $0.00398 |
| Sonnet 5 | $0.00001 | $0.00159 |
| Haiku 4.5 | $0.00000 | $0.00080 |
Grade A, and why
nano-banana 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Nano Banana
When to Use
Use this skill when the user explicitly wants to use Nano Banana, Gemini image models, Imagen-style generation/editing, Google AI Studio, or Atlas Cloud for image execution.
Use for:
- Text-to-image through a configured provider.
- Image editing with one or more local reference/source images.
- Comparing provider availability, privacy, and cost before execution.
- Running a generation/editing task after an image design brief is ready.
Do not use this skill as a generic prompt-writing guide. Use image-design for planning and route here only when execution through this provider is appropriate and available.
Provider And Privacy Rules
- Check which credentials are configured before choosing a provider.
- If multiple providers are configured, ask the user which to use unless cost/privacy preference is already clear.
- If no credentials are configured, provide setup guidance but do not ask the user to paste secrets into chat.
- Before uploading any local image, ask for explicit confirmation and state the destination provider.
- State that prompts and uploaded images may be sent to third-party services.
- Ask for budget limits before batch generation or high-resolution runs.
- Save outputs to a user-visible local directory and return output paths.
First Checks
Check environment variables without printing secret values:
test -n "$ATLASCLOUD_API_KEY" && echo "ATLASCLOUD_API_KEY configured"
test -n "$GEMINI_API_KEY" && echo "GEMINI_API_KEY configured"
If the source skill's helper script is available in the local environment, prefer using it. Otherwise use the provider API documented for the user's configured provider.
Workflow
- Confirm task type: text-to-image or image edit.
- Confirm prompt, source/reference image paths, aspect ratio, resolution, output directory, and count.
- Choose provider:
- Atlas Cloud when configured and acceptable to the user.
- Google AI Studio / Gemini when configured and acceptable to the user.
- no execution when credentials are missing; return setup guidance and prompt/brief instead.
- Confirm cost, upload, and privacy before execution.
- Submit the request.
- Poll or wait for the result if asynchronous.
- Download or save generated outputs.
- Verify file existence and return paths plus provider/task metadata.
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.
- 12d ago First seen · 80 lines · 3 tokens per session scan A 4edb4e31e170
nano-banana is a skill published in the GitHub repository Orkas-AI/Orkas-Awesome-AgentSkills (13 stars, last pushed 2mo ago), licensed MIT. It adds 3 tokens to every session and 795 once invoked, about $0.0000 per session on Opus 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.
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