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 Yasuui/ystack --skill nano-banana-skillgit clone --depth 1 https://github.com/Yasuui/ystackWrote 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/yasuui/ystack/nano-banana-skill)<a href="https://agentmods.dev/skills/yasuui/ystack/nano-banana-skill"><img src="https://agentmods.dev/badge/skills/yasuui/ystack/nano-banana-skill/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/yasuui/ystack/nano-banana-skill"><img src="https://agentmods.dev/badge/skills/yasuui/ystack/nano-banana-skill.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 | $0.00071 | $0.02161 |
| Opus 5.5 | $0.00028 | $0.00864 |
| Sonnet 5.5 | $0.00014 | $0.00432 |
| Haiku 4.5 | $0.00007 | $0.00216 |
Grade A, and why
nano-banana-skill 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 1mo 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 — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Nano Banana Skill
The Artist Mindset
Nano Banana is not a prompt box. It is a collaborator. The quality of output is entirely determined by the quality of direction. Vague prompts produce vague images. Specific direction produces specific results.
Before generating any image, answer these four questions:
- What is the exact subject? (not "a developer" — "a terminal window on a dark glass desk, partially reflected, 3/4 angle from the left")
- What is the lighting source? (not "good lighting" — "single cool blue rim light from the top-left, ambient fill from below in warm amber, no direct frontal fill")
- What is the camera relationship to the subject? (close-up, wide establishing, isometric, top-down, eye level, slightly below)
- What mood or decade does this reference? (Apple keynote 2020, early Stripe.com, Vercel 2023, editorial NYT tech section)
If you cannot answer all four, you are not ready to generate.
Prompt Architecture
Structure every prompt in this order:
[Subject description] + [spatial positioning] + [lighting] + [surface/material] +
[background treatment] + [color palette] + [mood/atmosphere] + [technical specs] +
[negative prompt]
The negative prompt is as important as the positive
Always include a negative prompt. The default negative prompt for all brand assets:
--no stock photography, watermark, lens flare, excessive bokeh,
saturated gradients, cartoon, illustration style, human faces,
text overlaid, banner ads, corporate clipart, plugin icons,
emoji-style graphics, purple-to-blue AI gradient
Asset Types and Prompts
Substack Header Background
Goal: abstract, editorial, dark. The text sits on top — the background should not compete.
Prompt template:
Abstract architectural geometry, [specific material: brushed titanium / oxidized steel / dark concrete /
smoked glass] surface, extreme close-up macro photography, shallow focus, cool ambient light from
the left at 15 degrees, rich dark shadows, [accent color]-tinted atmospheric haze in the midground,
no horizon line, no recognisable objects, mood: late-night studio, editorial, considered
--ar 16:9 --no text, faces, plants, logos, purple gradients, stock look
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.
- 1mo ago First seen · 213 lines · 71 tokens per session scan A a6cba04001c4
nano-banana-skill is a skill published in the GitHub repository Yasuui/ystack (1 stars, last pushed 28d ago), licensed MIT. It adds 71 tokens to every session and 2,161 once invoked, about $0.0003 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 2026-08-31.
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