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Agent skill

Nano Banana Prompting Skill

by LeoYeAI in LeoYeAI/openclaw-master-skills

Transform natural language image requests into optimized structured prompts for Gemini image generation.

MITAuto-check passedMedia & Creative

Install Nano Banana Prompting Skill

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill nano-banana-prompting-skill -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills nano-banana-prompting-skill --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/nano-banana-prompting .claude/skills/nano-banana-prompting-skill && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
nano-banana-prompting-skill
GitHub stars
2.2k
Token cost
~5.3k tokens
SKILL.md length
765 words
Files
5 (incl. references, assets)
Skills in repo
1,235
Repo updated
First seen
Licence
MIT

At a glance

Transform natural language image requests into optimized structured prompts for Gemini image generation.

  • Works in 4 steps: Read the user's request — understand… → Detect the style — use the Style… → Build the structured JSON prompt —… → …
  • Tasks that involve Image generation
  • SKILL.md covers How to Use, Style Detection, Structured Prompt Templates and Prompting Rules (ALL styles), plus 3 more sections
  • Calls uv

What it does

Nano Banana Prompting Skill is an agent skill from LeoYeAI/openclaw-master-skills. Transform natural language image requests into optimized structured prompts for Gemini image generation. Automatically detects style and builds the perfect prompt — cinematic, illustration, anime, 3D, watercolor, product, and more.

Its SKILL.md is about 5.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files and assets (for example `README.md`, `_meta.json` and `references/prompt-guide.md`).

It sits in Media & Creative, covering Image generation and Prompt engineering. It works with Google Gemini. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.

When your agent uses it

  • Tasks that involve Image generation
  • Tasks that involve Prompt engineering

Example prompts

  • “/nano-banana-prompting-skill”

Requirements

  • A credential in GEMINI_API_KEY

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Read the user's request — understand what they want (subject, mood, style)
  2. Detect the style — use the Style Detection rules below
  3. Build the structured JSON prompt — follow the template for that style
  4. Call the generator

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Nano Banana Prompting Skill loads about 5.3k tokens when it runs, and up to ~6k if it reads all its reference files. Until then it costs about 65 tokens; SKILL.md has 765 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~65
When it runs · the whole SKILL.md, loaded when a task matches
~5.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 765 words, ~5,330 tokens.

Download SKILL.mdSave it as .claude/skills/nano-banana-prompting-skill/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
nano-banana-prompting-skill
description
Transform natural language image requests into optimized structured prompts for Gemini image generation. Automatically detects style and builds the perfect prompt — cinematic, illustration, anime, 3D, watercolor, product, and more.

Gemini Image Prompting

This skill transforms simple, natural image requests into optimized structured prompts that produce dramatically better results from Gemini 3 Pro Image.

Instead of sending a plain text prompt (which produces generic, "AI slop" results), this skill builds a structured JSON prompt with the right technical details for the detected style — camera specs for photography, art techniques for illustration, render settings for 3D, and more.

How to Use

When the user asks you to generate or edit an image:

  1. Read the user's request — understand what they want (subject, mood, style)
  2. Detect the style — use the Style Detection rules below
  3. Build the structured JSON prompt — follow the template for that style
  4. Call the generator:
bash
uv run {nano-banana-pro-dir}/scripts/generate_image.py \
  --prompt '<YOUR_JSON_PROMPT>' \
  --filename "<descriptive-name>.png" \
  --resolution 2K

Replace {nano-banana-pro-dir} with the path to the nano-banana-pro skill (typically bundled with OpenClaw).

For image editing (user provides a reference image):

bash
uv run {nano-banana-pro-dir}/scripts/generate_image.py \
  --prompt '<YOUR_JSON_PROMPT>' \
  --filename "<output-name>.png" \
  -i "/path/to/reference.png" \
  --resolution 2K
Security Note

The --filename argument should always be a simple file path constructed by the agent (e.g., gecko-running.png). Never pass unsanitized user input directly as the filename. The agent should derive a safe, descriptive filename from the context.

Output Location

Save images to the user's Desktop or the path they specify:

  • Default: ~/Desktop/<descriptive-name>.png
  • Use descriptive filenames: gecko-coding-night.png, not output.png

Style Detection

Detect the style from the user's request. Look for keywords, context, and intent:

StyleTrigger Keywords / Context
Cinematic / Photorealistic"photo", "realistic", "cinematic", "portrait", "street", "landscape", real-world scenes, people, animals in real settings
Product / Studio"product shot", "studio", "mockup", "packaging", "e-commerce", objects on clean backgrounds
Street / Documentary"candid", "street", "reportage", "documentary", "raw", urban scenes
Illustration / Digital Art"illustration", "digital art", "concept art", "fantasy art", "draw", "artwork"
Anime / Manga"anime", "manga", "cel shaded", "studio ghibli", Japanese animation style
3D / Pixar"3D", "Pixar", "render", "CGI", "clay", "isometric", cartoon characters
Watercolor / Traditional"watercolor", "oil painting", "sketch", "pencil", "pastel", "charcoal", traditional media
Minimalist / Graphic"logo", "icon", "flat", "minimal", "vector", "graphic design", "poster"
Surreal / Abstract"surreal", "abstract", "dreamlike", "psychedelic", "impossible", "Dalí"

If no style is obvious, default to Cinematic / Photorealistic — it's the most versatile and produces the best baseline quality.

If the user specifies a style explicitly, always respect that over auto-detection.

Structured Prompt Templates

🎬 Cinematic / Photorealistic
json
{
  "instruction": "<one-line description of the final image>",
  "subject": {
    "description": "<main subject in detail>",
    "clothing": "<if applicable>",
    "expression": "<facial expression or mood>",
    "pose": "<body position, action>",
    "details": "<distinguishing features, textures>"
  },
  "scene": {
    "setting": "<location/environment>",
    "key_elements": "<important objects in the scene>",
    "background": "<what's behind the subject>",
    "foreground": "<what's in front, if any>",
    "time_of_day": "<morning, golden hour, night, etc.>"
  },
  "photography": {
    "camera": "<Sony A7IV | Hasselblad X2D | Canon R5 | ARRI Alexa 65 | Leica M11>",
    "lens": "<24mm f/1.4 | 35mm f/1.4 | 50mm f/1.2 | 85mm f/1.8 | 135mm f/2>",
    "shot_type": "<wide | medium | close-up | extreme close-up | aerial | low angle>",
    "depth_of_field": "<shallow with bokeh | deep | tilt-shift>",
    "lighting": "<natural golden hour | chiaroscuro | rim light | neon | overcast soft | studio three-point>",
    "film_stock": "<Kodak Portra 400 | Fujifilm Pro 400H | Kodak Ektar 100 | CineStill 800T | Ilford HP5>",
    "texture": "<subtle film grain | clean digital | heavy grain>"
  },
  "mood": "<emotional atmosphere in one sentence>",
  "color_palette": "<dominant colors or color grading style>",
  "aspect_ratio": "<1:1 | 16:9 | 4:3 | 9:16 | 3:2>",
  "quality": "8K, photorealistic, cinematic, RAW photo",
  "negative": "no text, no watermark, no deformed faces, no extra limbs, no blurry"
}

Camera selection guide:

  • Portraits → Hasselblad X2D or Canon R5 + 85mm
  • Street/documentary → Leica M11 + 35mm
  • Landscapes/cinematic → Sony A7IV + 24mm or ARRI Alexa 65
  • Night/low light → Sony A7IV + 50mm f/1.2 + CineStill 800T
  • Fashion → Hasselblad X2D + 80mm
📸 Product / Studio
json
{
  "instruction": "<product shot description>",
  "subject": {
    "product": "<item name and type>",
    "material": "<glass, metal, fabric, wood, plastic, ceramic>",
    "color": "<product colors>",
    "details": "<logos, textures, unique features>"
  },
  "scene": {
    "backdrop": "<seamless white | gradient | textured surface | lifestyle context>",
    "surface": "<marble | wood | concrete | acrylic | fabric>",
    "props": "<complementary objects if any>"
  },
  "photography": {
    "camera": "Hasselblad X2D",
    "lens": "90mm f/3.2 macro",
    "shot_type": "<hero shot | flat lay | 45-degree | floating | exploded view>",
    "lighting": "<softbox | ring light | natural window | dramatic single source | backlit>",
    "reflections": "<subtle | mirror-like | none>",
    "depth_of_field": "shallow, product sharp, background soft"
  },
  "mood": "<clean and premium | warm and inviting | bold and modern>",
  "color_palette": "<brand-aligned colors>",
  "aspect_ratio": "1:1",
  "quality": "8K, commercial photography, sharp detail, color-accurate",
  "negative": "no text, no watermark, no dust, no scratches"
}
🖌️ Illustration / Digital Art
json
{
  "instruction": "<illustration description>",
  "subject": {
    "character": "<character description>",
    "expression": "<emotion>",
    "pose": "<action or stance>",
    "details": "<costume, accessories, distinctive features>"
  },
  "scene": {
    "setting": "<world/environment>",
    "key_elements": "<important scene elements>",
    "atmosphere": "<fog, particles, light rays, sparks>"
  },
  "art_style": {
    "medium": "<digital painting | concept art | comic book | storybook | gouache>",
    "technique": "<cel shading | painterly | line art with color | crosshatching>",
    "reference_artists": "<1-2 artist names for style reference>",
    "color_approach": "<vibrant saturated | muted earthy | monochromatic | complementary>"
  },
  "composition": {
    "framing": "<rule of thirds | centered | dynamic diagonal | symmetrical>",
    "perspective": "<eye level | bird's eye | worm's eye | isometric>",
    "focal_point": "<where the eye should go>"
  },
  "mood": "<epic and dramatic | whimsical and playful | dark and moody>",
  "color_palette": "<specific colors or palette description>",
  "aspect_ratio": "<16:9 | 3:2 | 1:1>",
  "quality": "highly detailed illustration, trending on ArtStation, masterpiece",
  "negative": "no text, no watermark, no photo-realistic, no AI artifacts"
}
🌸 Anime / Manga
json
{
  "instruction": "<anime scene description>",
  "subject": {
    "character": "<character description>",
    "hair": "<style, color>",
    "eyes": "<color, expression>",
    "outfit": "<clothing details>",
    "pose": "<action or expression>"
  },
  "scene": {
    "setting": "<location>",
    "atmosphere": "<cherry blossoms, rain, sunset glow, sparkles>",
    "background_style": "<detailed | simplified | gradient wash>"
  },
  "art_style": {
    "studio_reference": "<Studio Ghibli | Kyoto Animation | Ufotable | MAPPA | Trigger | Makoto Shinkai>",
    "line_weight": "<thin and clean | bold and expressive | variable>",
    "shading": "<flat cel | soft gradient | dramatic shadow>",
    "era": "<modern anime | 90s retro | 80s vintage>"
  },
  "composition": {
    "framing": "<close-up portrait | action shot | scenic wide | over-the-shoulder>",
    "effects": "<speed lines | light flares | motion blur | sparkle overlay>"
  },
  "mood": "<heartwarming | intense action | melancholic | comedic>",
  "color_palette": "<pastel soft | vibrant saturated | dark moody | warm sunset>",
  "aspect_ratio": "16:9",
  "quality": "anime key visual, high detail, studio quality, clean lines",
  "negative": "no western cartoon style, no 3D, no photorealistic, no deformed hands"
}
🧸 3D / Pixar / CGI
json
{
  "instruction": "<3D scene description>",
  "subject": {
    "character": "<character description>",
    "material": "<clay | plastic | rubber | fur | fabric>",
    "proportions": "<chibi | realistic | stylized | exaggerated>",
    "expression": "<emotion>",
    "pose": "<stance or action>"
  },
  "scene": {
    "environment": "<setting>",
    "props": "<objects in scene>",
    "scale": "<miniature diorama | life-size | macro>"
  },
  "render": {
    "engine": "<Pixar RenderMan | Blender Cycles | Unreal Engine 5 | Octane>",
    "style": "<Pixar | DreamWorks | Aardman claymation | Nendoroid figure>",
    "materials": "<subsurface scattering | glossy plastic | matte clay | fabric texture>",
    "lighting": "<three-point studio | HDRI environment | dramatic rim | soft ambient>",
    "effects": "<ambient occlusion | global illumination | volumetric fog | depth of field>"
  },
  "mood": "<playful and colorful | dramatic and cinematic | cozy and warm>",
  "color_palette": "<bright primary | pastel | earthy muted | vibrant candy>",
  "aspect_ratio": "16:9",
  "quality": "3D render, Pixar quality, high detail, raytraced, 8K",
  "negative": "no 2D, no flat, no sketch, no watermark, no text"
}
🎨 Watercolor / Traditional Art
json
{
  "instruction": "<painting description>",
  "subject": {
    "description": "<what to paint>",
    "details": "<important visual details>"
  },
  "scene": {
    "setting": "<environment or abstract>",
    "elements": "<supporting visual elements>"
  },
  "traditional_art": {
    "medium": "<watercolor | oil paint | acrylic | gouache | pastel | charcoal | pencil | ink wash>",
    "paper": "<cold press watercolor | hot press | canvas | toned paper | rice paper>",
    "technique": "<wet-on-wet | dry brush | impasto | glazing | stippling | hatching | wash>",
    "brush": "<round | flat | fan | palette knife | sponge>",
    "finish": "<loose and expressive | tight and detailed | abstract | impressionistic>"
  },
  "composition": {
    "framing": "<centered | rule of thirds | asymmetric>",
    "white_space": "<generous margins | edge-to-edge | vignette>"
  },
  "mood": "<serene | vibrant | nostalgic | raw and emotional>",
  "color_palette": "<limited palette (3-4 colors) | full spectrum | monochrome wash>",
  "aspect_ratio": "3:2",
  "quality": "fine art quality, museum worthy, masterful brushwork, visible texture",
  "negative": "no digital look, no photorealistic, no text, no watermark, no AI artifacts"
}
✏️ Minimalist / Graphic Design
json
{
  "instruction": "<design description>",
  "subject": {
    "element": "<main visual element>",
    "style": "<geometric | organic | typographic | iconographic>"
  },
  "design": {
    "approach": "<flat design | material design | swiss style | brutalist | retro>",
    "grid": "<centered | asymmetric | modular grid>",
    "shapes": "<circles | rectangles | triangles | organic blobs | line art>",
    "contrast": "<high contrast | subtle | duotone>"
  },
  "color_palette": "<2-3 specific colors or palette name>",
  "background": "<solid color | gradient | textured | transparent>",
  "aspect_ratio": "1:1",
  "quality": "clean vector quality, crisp edges, professional graphic design",
  "negative": "no photorealistic, no complex textures, no gradients unless specified, no text unless requested"
}
🌀 Surreal / Abstract
json
{
  "instruction": "<surreal scene description>",
  "subject": {
    "central_element": "<main impossible or dreamlike element>",
    "transformation": "<how reality is bent or broken>",
    "symbolism": "<underlying meaning or motif>"
  },
  "scene": {
    "reality_level": "<slightly off | dreamlike | fully impossible | cosmic>",
    "environment": "<melting landscape | infinite space | underwater sky | fractal world>",
    "scale_distortion": "<giant small things | tiny big things | impossible geometry>"
  },
  "art_style": {
    "reference": "<Dalí | Magritte | Escher | Beksinski | James Jean | Android Jones>",
    "medium": "<hyperrealistic oil | digital surrealism | mixed media | collage>",
    "technique": "<photobashing | matte painting | double exposure | glitch>"
  },
  "mood": "<unsettling beauty | peaceful absurdity | cosmic wonder | dark fantasy>",
  "color_palette": "<describe the color world>",
  "aspect_ratio": "16:9",
  "quality": "highly detailed, surrealist masterpiece, museum quality",
  "negative": "no text, no watermark, no generic, no stock photo feel"
}

Prompting Rules (ALL styles)

Always Do:
  1. Be specific — "mint-green gecko" not "a gecko", "warm golden hour" not "nice light"
  2. Include negative prompt — always specify what to avoid
  3. Pick an aspect ratio — match the content (landscape 16:9, portrait 9:16, square 1:1)
  4. Add sensory details — textures, temperatures, sounds implied visually
  5. One focal point — every image needs a clear subject
  6. Match quality keywords to style — "8K photorealistic" for photos, "trending on ArtStation" for illustrations
  7. Color palette matters — specify colors or a grading reference, don't leave it to chance
Show full SKILL.md (269 more words)Show less
Never Do:
  1. ❌ Don't include text/words in the image (Gemini renders text poorly)
  2. ❌ Don't over-specify (pick 1 camera, 1 lens, not 3 options)
  3. ❌ Don't mix conflicting styles ("photorealistic anime" — pick one)
  4. ❌ Don't use generic prompts ("a beautiful sunset" — add specifics)
  5. ❌ Don't forget the negative prompt
  6. ❌ Don't request NSFW content
Resolution Guide:
  • 1K — Quick drafts, thumbnails, iteration
  • 2K — Social media, general use (best quality/cost ratio)
  • 4K — Print, hero images, final deliverables

Examples

User says: "make me a gecko eating pizza on a skateboard"

Detected style: 3D / Pixar (fun character scene)

json
{
  "instruction": "A cute mint-green gecko character riding a skateboard while eating a large slice of pepperoni pizza",
  "subject": {
    "character": "small mint-green gecko with big expressive pale pink eyes and a wide happy grin",
    "material": "smooth glossy skin with subtle subsurface scattering",
    "proportions": "stylized chibi, slightly oversized head",
    "expression": "pure joy, eyes half-closed savoring the pizza",
    "pose": "standing on a moving skateboard, one hand holding pizza slice, cheese stretching"
  },
  "scene": {
    "environment": "sunny Venice Beach boardwalk, palm trees, blue sky",
    "props": "worn wooden skateboard with flame decals, pizza box on a nearby bench",
    "scale": "life-size character in real-world setting"
  },
  "render": {
    "engine": "Pixar RenderMan",
    "style": "Pixar",
    "materials": "subsurface scattering on gecko skin, glossy cheese, matte concrete",
    "lighting": "warm golden hour sunlight with soft shadows",
    "effects": "subtle motion blur on skateboard wheels, depth of field"
  },
  "mood": "carefree summer vibes, playful and fun",
  "color_palette": "warm yellows, mint green, sky blue, pizza orange",
  "aspect_ratio": "16:9",
  "quality": "3D render, Pixar quality, high detail, raytraced, 8K",
  "negative": "no 2D, no flat, no text, no watermark, no dark mood"
}
User says: "a samurai in the rain, dramatic"

Detected style: Cinematic / Photorealistic

json
{
  "instruction": "A lone samurai standing in heavy rain at night, katana drawn, cinematic and dramatic",
  "subject": {
    "description": "weathered samurai warrior, middle-aged, battle-scarred",
    "clothing": "dark indigo hakama and kimono, worn leather chest armor, straw sandals",
    "expression": "intense focus, eyes locked forward, jaw set",
    "pose": "standing in chudan-no-kamae stance, katana held at center",
    "details": "rain droplets on blade edge catching light, wet hair tied in topknot"
  },
  "scene": {
    "setting": "abandoned temple courtyard with stone lanterns",
    "key_elements": "heavy rainfall, puddles reflecting lantern light",
    "background": "dark silhouette of temple roof, mist rolling between pillars",
    "foreground": "rain splashing on stone ground, shallow puddles",
    "time_of_day": "deep night, moonless"
  },
  "photography": {
    "camera": "ARRI Alexa 65",
    "lens": "50mm f/1.2",
    "shot_type": "medium shot, slightly low angle",
    "depth_of_field": "shallow, rain drops in foreground out of focus",
    "lighting": "single warm lantern as key light from left, cool rain backlit by distant light, rim light on shoulders",
    "film_stock": "CineStill 800T",
    "texture": "subtle film grain"
  },
  "mood": "tense calm before the storm, honor and solitude",
  "color_palette": "deep indigo, warm amber lantern glow, cool steel grey",
  "aspect_ratio": "16:9",
  "quality": "8K, photorealistic, cinematic, RAW photo",
  "negative": "no text, no watermark, no deformed faces, no blurry, no anime style"
}
User says: "paint me some sunflowers, like watercolor"

Detected style: Watercolor / Traditional

json
{
  "instruction": "A loose, expressive watercolor painting of sunflowers in a ceramic vase",
  "subject": {
    "description": "five sunflowers in various stages of bloom, some drooping slightly",
    "details": "petals ranging from bright yellow to burnt sienna, dark brown seed heads with visible texture"
  },
  "scene": {
    "setting": "simple tabletop, implied window light",
    "elements": "rustic blue ceramic vase, a few fallen petals on the surface, soft shadow"
  },
  "traditional_art": {
    "medium": "watercolor",
    "paper": "cold press watercolor paper, visible tooth texture",
    "technique": "wet-on-wet for soft petal edges, dry brush for seed head texture, controlled wash for background",
    "brush": "large round for washes, small round for details",
    "finish": "loose and expressive, some areas left unfinished showing white paper"
  },
  "composition": {
    "framing": "slightly off-center, flowers extending above frame edge",
    "white_space": "generous margins, breathing room around the vase"
  },
  "mood": "warm, nostalgic, peaceful afternoon light",
  "color_palette": "cadmium yellow, burnt sienna, cerulean blue, sap green — limited palette",
  "aspect_ratio": "3:2",
  "quality": "fine art quality, museum worthy, masterful brushwork, visible paper texture",
  "negative": "no digital look, no photorealistic, no text, no watermark, no harsh edges"
}

Editing / Reference Images

When the user provides a reference image to edit or use as inspiration:

  1. Include the image with -i flag
  2. Describe what to change in the instruction field
  3. Keep the JSON structure — same style detection and template applies
  4. Be explicit about what to preserve vs change
bash
uv run {nano-banana-pro-dir}/scripts/generate_image.py \
  --prompt '<JSON_PROMPT>' \
  --filename "edited-output.png" \
  -i "/path/to/original.png" \
  --resolution 2K

For character consistency across multiple images, always include the same reference image(s) and describe the character identically in the subject field.

Quick Reference: Style → Key Fields

StyleMust-Have FieldsKey Differentiator
Cinematicphotography.camera, lens, film_stockReal camera specs sell the realism
Productphotography.lighting, surface, reflectionsClean, commercial precision
Illustrationart_style.medium, technique, reference_artistsArtist references anchor the style
Animeart_style.studio_reference, line_weight, shadingStudio name = instant style match
3D/Pixarrender.engine, style, materialsRender engine + material physics
Watercolortraditional_art.medium, paper, techniquePaper texture + brush technique
Minimalistdesign.approach, shapes, contrastLess is more, precision matters
Surrealart_style.reference, reality_level, transformationNamed surrealist = instant vibe

© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (references, assets) in skills/nano-banana-prompting of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • README.md
  • _meta.json
  • assets/banner.svg
  • references/prompt-guide.md

Open the folder on GitHubat commit e5199b5

Compare with similar skills

Nano Banana Prompting Skill next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Nano Banana Prompting Skill compared with similar skills
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Gemini Web Reverse-Engineered ClientJimLiu/baoyu-skills27k5 repos~1.6kAutomated safety check: PassMIT

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Works with

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Questions about Nano Banana Prompting Skill

What does Nano Banana Prompting Skill do?

Transform natural language image requests into optimized structured prompts for Gemini image generation. Nano Banana Prompting Skill is an agent skill from LeoYeAI/openclaw-master-skills. Transform natural language image requests into optimized structured prompts for Gemini image generation.

When should I use Nano Banana Prompting Skill?

Nano Banana Prompting Skill fits situations like: tasks that involve Image generation; tasks that involve Prompt engineering.

How do I install Nano Banana Prompting Skill in Claude Code?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill nano-banana-prompting-skill -a claude-code`. Or copy the skill folder (skills/nano-banana-prompting in LeoYeAI/openclaw-master-skills) into .claude/skills/nano-banana-prompting-skill in your project. Claude Code loads it when a task matches its description.

How do I install Nano Banana Prompting Skill in Codex?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill nano-banana-prompting-skill -a codex`. Or copy the skill folder (skills/nano-banana-prompting in LeoYeAI/openclaw-master-skills) into .agents/skills/nano-banana-prompting-skill in your project. Codex loads it when a task matches its description.

Can I use Nano Banana Prompting Skill in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill nano-banana-prompting-skill -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/nano-banana-prompting-skill, .gemini/skills/nano-banana-prompting-skill, .github/skills/nano-banana-prompting-skill and .opencode/skills/nano-banana-prompting-skill in your project.

What does Nano Banana Prompting Skill need to run?

Going by SKILL.md and its folder, Nano Banana Prompting Skill needs the command-line tools its instructions call (uv). Our summary lists: A credential in GEMINI_API_KEY.

Does Nano Banana Prompting Skill access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Nano Banana Prompting Skill safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Nano Banana Prompting Skill use?

Nano Banana Prompting Skill is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Nano Banana Prompting Skill use?

About 5.3k tokens (SKILL.md is roughly 21k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 708 tokens, read only when the agent opens those files.

What are the alternatives to Nano Banana Prompting Skill?

Skills that share tags, products or a category with Nano Banana Prompting Skill: Bananahub (bananahub-ai/bananahub-skill, 118 stars), Image Ad Clone (krusemediallc/arcads-claude-code, 1.6k stars), AI Image Generation and Editing (zhayujie/CowAgent, 47k stars) and Logo Generator (op7418/logo-generator-skill, 2.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Nano Banana Prompting Skill?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,161 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.