Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/aiagentwithdhruv/skillsnpx agentmods add skills/aiagentwithdhruv/skills/nano-banana-imagesWrote 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/aiagentwithdhruv/skills/nano-banana-images)<a href="https://agentmods.dev/skills/aiagentwithdhruv/skills/nano-banana-images"><img src="https://agentmods.dev/badge/skills/aiagentwithdhruv/skills/nano-banana-images/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/aiagentwithdhruv/skills/nano-banana-images"><img src="https://agentmods.dev/badge/skills/aiagentwithdhruv/skills/nano-banana-images.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00043 | $0.02543 |
| Opus 5 | $0.00022 | $0.01272 |
| Sonnet 5 | $0.00009 | $0.00509 |
| Haiku 4.5 | $0.00004 | $0.00254 |
Grade A, and why
Nano Banana 2 Image Generation Master 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 — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Nano Banana 2 Image Generation Master
Goal
The purpose of this skill is to provide a standardized, highly controlled method for generating images using AI model Nano Banana 2 (or any underlying model connected to the generate_image tool). By strictly enforcing a structured JSON parameter schema, this skill neutralizes native model biases (like over-smoothing, dataset-averaging, or "plastic" AI styling) and ensures raw, unretouched, hyper-realistic outputs.
Prerequisites
- fal.ai API key (
FAL_KEYin.env) — sign up at https://fal.ai (free tier, Nano Banana 2 model) - OR Euri API key (
EURI_API_KEYin.env) — free for Euron students at https://euron.one/euri - A clear understanding of the user's desired Subject, Lighting, and Camera characteristics.
Core Schema Structure
When constructing a prompt for the generate_image tool, you MUST use the following JSON schema as the foundation. Fill in the string values with extreme, microscopic detail.
{
"task": "string - High-level goal (e.g., 'sports_selfie_collage', 'single_macro_portrait')",
"output": {
"type": "string - e.g., 'single_image', '4-panel_collage'",
"layout": "string - e.g., '1x1', '2x2_grid', 'side-by-side'",
"aspect_ratio": "string - e.g., '3:4', '16:9', '4:5'",
"resolution": "string - e.g., 'ultra_high', 'medium_low'",
"camera_style": "string - e.g., 'smartphone_front_camera', 'professional_dslr'"
},
"image_quality_simulation": {
"sharpness": "string - e.g., 'tack_sharp', 'slightly_soft_edges'",
"noise": "string - e.g., 'unfiltered_sensor_grain', 'visible_film_grain', 'clean_digital'",
"compression_artifacts": "boolean - true if attempting to simulate uploaded UGC",
"dynamic_range": "string - e.g., 'limited', 'hdr_capable'",
"white_balance": "string - e.g., 'slightly_warm', 'cool_fluorescent'",
"lens_imperfections": [
"array of strings - e.g., 'subtle chromatic aberration', 'minor lens distortion', 'vignetting'"
]
},
"subject": {
"type": "string - e.g., 'human_portrait', 'nature_macro', 'infographic_flatlay'",
"human_details": {
"//": "Use this block ONLY for human subjects",
"identity": "string",
"appearance": "string - Extremely specific (e.g., visible pores, mild redness)",
"outfit": "string"
},
"object_or_nature_details": {
"//": "Use this block for non-human subjects",
"material_or_texture": "string - e.g., 'brushed aluminum', 'dew-covered velvety petals'",
"wear_and_tear": "string - e.g., 'subtle scratches on the anodized finish', 'browning edges on leaves'",
"typography": "string - e.g., 'clean sans-serif overlaid text, perfectly legible'"
}
},
"multi_panel_layout": {
"grid_panels": [
{
"panel": "string - e.g., 'top_left', 'full_frame' (if not a grid)",
"pose": "string - e.g., 'slight upward selfie angle, relaxed smile'",
"action": "string - e.g., 'holding phone with one hand, casual posture'"
}
]
},
"environment": {
"location": "string - e.g., 'gym or outdoor sports area'",
"background": "string - What is behind the subject (e.g., 'blurred gym equipment')",
"lighting": {
"type": "string - e.g., 'natural or overhead gym lighting', 'harsh direct sunlight'",
"quality": "string - e.g., 'uneven, realistic, non-studio', 'high-contrast dramatic'"
}
},
"embedded_text_and_overlays": {
"text": "string (optional)",
"location": "string (optional)"
},
"structural_preservation": {
"preservation_rules": [
"array of strings - e.g., 'Exact physical proportions must be preserved'"
]
},
"controlnet": {
"pose_control": {
"model_type": "string - e.g., 'DWPose'",
"purpose": "string",
"constraints": ["array of strings"],
"recommended_weight": "number"
},
"depth_control": {
"model_type": "string - e.g., 'ZoeDepth'",
"purpose": "string",
"constraints": ["array of strings"],
"recommended_weight": "number"
}
},
"explicit_restrictions": {
"no_professional_retouching": "boolean - typically true for realism",
"no_studio_lighting": "boolean - typically true for candid shots",
"no_ai_beauty_filters": "boolean - mandatory true to avoid plastic look",
"no_high_end_camera_look": "boolean - true if simulating smartphones"
},
"negative_prompt": {
"forbidden_elements": [
"array of strings - Massive list of 'AI style' blockers required for extreme realism. Example stack: 'anatomy normalization', 'body proportion averaging', 'dataset-average anatomy', 'wide-angle distortion not in reference', 'lens compression not in reference', 'cropping that removes volume', 'depth flattening', 'mirror selfies', 'reflections', 'beautification filters', 'skin smoothing', 'plastic skin', 'airbrushed texture', 'stylized realism', 'editorial fashion proportions', 'more realistic reinterpretation'"
]
}
}
What ships with it
4 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.
- 12d ago First seen · 188 lines · 43 tokens per session scan A a743b7efac8c
Nano Banana 2 Image Generation Master is a skill published in the GitHub repository aiagentwithdhruv/skills (17 stars, last pushed 15d ago), licensed MIT. It adds 43 tokens to every session and 2,543 once invoked, about $0.0002 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…