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图像处理敏感数据clawhub未标认证来源可访问clear审计通过

nano-banana-pro-image纳米香蕉临图像

Agent Skill

用于辅助图像生成、图片编辑、视觉素材处理或图像模型工作流。它适合让 Agent 根据文本生成图片、处理背景、整理视觉提示词或调用相关图像工具。使用时需要确认输入图片、版权来源、输出格式和模型限制;涉及人物、品牌、商品或公开展示素材时,应额外核对授权、真实性和内容合规边界。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:nano-banana-pro-image(纳米香蕉临图像)
来源仓库:https://github.com/xixihhhh/nano-banana-pro-image
安装命令:
openclaw skills install nano-banana-pro-image
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install nano-banana-pro-image

简介

用于辅助图像生成、图片编辑、视觉素材处理或图像模型工作流。

  • 适合让 Agent 根据文本生成图片、处理背景、整理视觉提示词或调用相关图像工具。
  • 通过 clawhub 安装并使用 openclaw skills install nano-banana-pro-image 命令部署。
  • 使用时需要确认输入图片、版权来源、输出格式和模型限制;涉及人物、品牌、商品或公开展示素材时,应额外核对授权、真实性和内容合规边界。
  • 可结合来源仓库和原始 README 继续核验具体用法和功能细节。

SKILL.md

name
nano-banana-pro
description
Generate and edit images using Google's Nano Banana Pro (Gemini 3 Pro Image / Imagen Pro) — the premium AI image generation model optimized for professional asset production with advanced reasoning ('Thinking'), high-fidelity text rendering, and complex multi-turn creation. Supports text-to-image and image editing with up to 6 reference images, resolutions up to 4K, and 14+ aspect ratios. Available via Atlas Cloud API. Use this skill whenever the user wants to generate high-quality professional images, create AI art with precise text, edit photos with AI, produce marketing assets, infographics, menus, diagrams, or any visual content requiring detailed text rendering. Also trigger when users mention Nano Banana Pro, Gemini 3 Pro Image, Imagen Pro, or ask for premium/professional-grade AI image generation, concept art, product photography, or visual assets with complex compositions.
source
https://github.com/AtlasCloudAI/nano-banana-2-skill
homepage
https://github.com/AtlasCloudAI/nano-banana-2-skill
metadata
openclaw
requires
env
primaryEnv
ATLASCLOUD_API_KEY

Nano Banana Pro Image Generation & Editing

Generate and edit images using Google's Nano Banana Pro (Gemini 3 Pro Image) — the premium AI image generation model designed for professional asset production, utilizing advanced reasoning ("Thinking") to follow complex instructions and render high-fidelity text in images.

Nano Banana Pro excels at infographics, menus, diagrams, marketing assets, and any task requiring precise text rendering and complex multi-object composition.

Data usage note: This skill sends text prompts and image URLs/data to Atlas Cloud API for image generation. No data is stored locally beyond the downloaded output files.
Security note: API keys are read exclusively from environment variables (ATLASCLOUD_API_KEY) and passed via HTTP headers — never embedded in URL query strings or command arguments. All user-provided text (prompts, file paths) must be passed through JSON request bodies to prevent shell injection. When constructing curl commands, always use a JSON payload (-d '{...}') rather than string interpolation in the shell. File paths should be validated before use. The skill does not execute any user-provided code — it only sends structured API requests and downloads output files.

Nano Banana Pro vs Nano Banana 2

FeatureNano Banana ProNano Banana 2
FocusProfessional quality, complex tasksSpeed, high-volume generation
Text renderingSuperior — best for infographics, menusGood
Thinking modeEnabled by defaultNot available
Reference images (object)Up to 6Up to 10
Character consistency imagesUp to 5Up to 14
ResolutionUp to 4KUp to 4K

Choose Nano Banana Pro when quality and text precision matter. Choose Nano Banana 2 when speed and cost matter.


Pricing

ResolutionAtlas Cloud StandardAtlas Cloud Developer
1K$0.126$0.098
2K$0.126$0.098
4K$0.126$0.098

Atlas Cloud uses flat-rate pricing regardless of resolution.


Available Models

Model IDTierPriceBest For
google/nano-banana-pro/text-to-imageStandard$0.126/imageProduction, high-quality output
google/nano-banana-pro/text-to-image-developerDeveloper$0.098/imagePrototyping, experiments
google/nano-banana-pro/editStandard$0.126/imageProduction editing
google/nano-banana-pro/edit-developerDeveloper$0.098/imageBudget editing, experiments

Atlas Cloud API

Setup

  1. Sign up at https://www.atlascloud.ai
  2. Console → API Keys → Create new key
  3. Set env: export ATLASCLOUD_API_KEY="your-key"

Parameters

Text-to-Image:

ParameterTypeRequiredDefaultOptions
promptstringYes-Image description
aspect_ratiostringNo1:11:1, 3:2, 2:3, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9
resolutionstringNo1k1k, 2k, 4k
output_formatstringNopngpng, jpeg

Image Editing — same as above plus:

ParameterTypeRequiredDescription
imagesarray of stringsYes1-10 image URLs to edit

Workflow: Submit → Poll → Download

# Step 1: Submit
curl -s -X POST "https://api.atlascloud.ai/api/v1/model/generateImage" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "google/nano-banana-pro/text-to-image",
    "prompt": "A professional infographic showing quarterly revenue growth with bar charts and annotations",
    "aspect_ratio": "16:9",
    "resolution": "2k"
  }'
# Returns: { "code": 200, "data": { "id": "prediction-id" } }

# Step 2: Poll (every 3-5 seconds until "completed" or "succeeded")
curl -s "https://api.atlascloud.ai/api/v1/model/prediction/{prediction-id}" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY"
# Returns: { "code": 200, "data": { "status": "completed", "outputs": ["https://...url..."] } }

# Step 3: Download
curl -o output.png "IMAGE_URL_FROM_OUTPUTS"

Image editing example:

curl -s -X POST "https://api.atlascloud.ai/api/v1/model/generateImage" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "google/nano-banana-pro/edit",
    "prompt": "Replace the text on the sign with: Grand Opening Sale — 50% Off",
    "images": ["https://example.com/storefront.jpg"],
    "resolution": "2k"
  }'

Polling logic:

  • processing / starting / running → wait 3-5s, retry (Pro model may take longer than Nano Banana 2 due to Thinking mode)
  • completed / succeeded → done, get URL from data.outputs[]
  • failed → error, read data.error

Atlas Cloud MCP Tools (if available)

If the Atlas Cloud MCP server is configured, use built-in tools:

atlas_quick_generate(model_keyword="nano banana pro", type="Image", prompt="...")
atlas_generate_image(model="google/nano-banana-pro/text-to-image", params={...})
atlas_get_prediction(prediction_id="...")

Implementation Guide

  1. Check API key: Ensure ATLASCLOUD_API_KEY is set. If not, ask the user to sign up at https://www.atlascloud.ai and set export ATLASCLOUD_API_KEY="your-key".
  1. Extract parameters:

- Prompt: the image description — Nano Banana Pro handles complex, detailed prompts well - Aspect ratio: infer from context (infographic→3:4 or 9:16, banner→16:9, menu→3:4, social post→1:1) - Resolution: default 1k, use 2k/4k for professional output - For editing: identify source image URL(s) or local file path

  1. Choose model tier:

- Standard for production use - Developer if user wants to save costs or is experimenting

  1. Sanitize inputs: Ensure user-provided prompts and file paths do not contain shell metacharacters. Always pass prompts inside JSON payloads (never via shell interpolation). Validate that image file paths exist and are readable before encoding.
  1. Execute: POST to Atlas Cloud generateImage API → poll prediction (may take 10-30s due to Thinking mode) → download result
  1. Present result: show file path, offer to open

Prompt Tips for Nano Banana Pro

Nano Banana Pro excels at understanding complex, structured prompts. Take advantage of its Thinking mode:

  • Text in images: Include exact text in quotes — Pro renders text with high fidelity. Example: "A cafe chalkboard menu reading: 'Today's Special — Matcha Latte $5.50'"
  • Infographics: Describe data, layout, and annotations. Example: "An infographic showing 3 steps of coffee brewing with numbered icons and captions"
  • Marketing assets: Specify brand colors, text placement, and style. Example: "A product banner with dark background, gold accents, text 'Limited Edition' top-center"
  • Complex compositions: Describe spatial relationships and multiple objects. Example: "A still life with a ceramic vase left-center, three oranges arranged in front, and a linen cloth draped over the table edge"
  • Style: "photorealistic", "editorial illustration", "minimalist flat design", "watercolor"
  • Lighting: "studio lighting", "natural window light", "dramatic chiaroscuro"

适合场景

01

文本生成图片

02

图片风格化

03

产品图和创意图

04

需要 FLUX 模型时

能力概览

能力 1

调用 FLUX 图像模型

能力 2

支持文本生图和图像改写

能力 3

覆盖 LoRA 或风格适配

能力 4

适合创意视觉生成

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

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按下载量换算4,535

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敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

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来源信息

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