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moai-connector-nano-banana摩艾连接器纳米香蕉

Agent Skill

moai-connector-nano-banana 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

247

周安装

10

GitHub Stars

1

下载量

78
CodexClaudeCursorGemini CLI

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

3

许可证

MIT

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:moai-connector-nano-banana(摩艾连接器纳米香蕉)
来源仓库:https://github.com/rdmptv/adbautoplayer
仓库路径:skills/moai-connector-nano-banana
安装命令:
npx skills add https://github.com/rdmptv/adbautoplayer --skill moai-connector-nano-banana
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/rdmptv/adbautoplayer --skill moai-connector-nano-banana

简介

用于查找、检索和筛选相关信息,适合基于关键词或场景定位内容。

  • 支持多轮交互和结果精炼,便于 Agent 在复杂任务中提取事实。
  • 通过 GitHub 安装,需确认是否会触发联网或执行系统命令。
  • 权限和稳定性未经验证,建议在使用前人工复核输出。
  • 适用于 Codex、Claude、Cursor 和 Gemini CLI,实际能力依赖仓库实现。

SKILL.md

🎯 Quick Reference (30 seconds)

Purpose: Professional image generation using Google's Nano Banana Pro (Gemini 3 Pro Image Preview).

Key Features:

  • Text-to-Image: Detailed prompts → 1K/2K/4K resolution images
  • Image-to-Image: Style transfer, object manipulation, editing
  • Real-time Grounding: Google Search integration for factual content
  • Multi-Reference: Up to 14 reference images (6 objects + 5 humans)
  • Advanced Text: Sophisticated text rendering directly in images

Two Models:

  1. Nano Banana Pro (gemini-3-pro-image-preview) - Professional quality, 10-60s
  2. Gemini 2.5 Flash (gemini-2.5-flash-image) - Fast, ~5-15s

Implementation Guide (5 minutes)

Features

  • Text-to-Image generation with 1K/2K/4K resolutions
  • Image-to-Image editing and style transfer
  • Multi-turn refinement for iterative improvements
  • Reference image guidance (up to 14 references)
  • Real-time Google Search grounding for factual content
  • Advanced text rendering directly in images

When to Use

  • Generating professional visual assets for documentation or marketing
  • Creating UI mockups and design concepts quickly
  • Producing social media graphics and promotional images
  • Illustrating technical documentation with custom diagrams
  • Rapid prototyping of visual ideas before final design work

Core Patterns

Pattern 1: Structured Prompt for Quality

prompt = """
A serene Japanese garden at golden hour.
Lighting: warm sunset light filtering through maple trees.
Camera: wide-angle 35mm lens, low angle shot.
Composition: Rule of thirds, stone path leading to pagoda.
Color palette: warm gold, jade green, soft cream.
Style: photorealistic with slight cinematic color grading.
Quality: 4K resolution. Final output: PNG.
"""

Pattern 2: Multi-Turn Refinement

  1. Generate initial image with base prompt
  2. Review output and identify areas for improvement
  3. Provide targeted refinement: "Make sky more dramatic"
  4. Iterate up to 5 turns for perfect result

Pattern 3: Reference-Guided Generation

# Use reference images to guide style
generate_image(
    prompt="Mountain landscape in the style of reference",
    reference_images=["style_ref.png", "composition_ref.png"],
    resolution="2K",
    aspect_ratio="16:9"
)

📚 Core Patterns (5-10 minutes)

Pattern 1: Prompt Structure for Quality Images

Key Concept: Well-structured prompts generate better images

Template:

[Scene Description]
A [adjective] [subject] doing [action].
Setting: [location] with [environmental details].

[Photographic Elements]
Lighting: [type], creating [mood].
Camera: [angle] shot with [lens] lens.
Composition: [framing_details].

[Color & Style]
Color palette: [colors]. Style: [art_style].
Quality: [resolution]. Final output: [format].

Example:

A serene Japanese garden at golden hour.
Lighting: warm sunset light, creating peaceful mood.
Camera: wide-angle 35mm lens shot.
Color palette: gold, jade green, cream.
Quality: 4K photorealistic. Final: PNG.

Pattern 2: Text-to-Image Generation

Key Concept: Generate professional images from text prompts

Basic Flow:

  1. Write detailed, structured prompt
  2. Choose resolution (1K, 2K, 4K)
  3. Select aspect ratio (1:1, 16:9, 3:2, etc.)
  4. Enable Google Search for current information (optional)
  5. Generate and retrieve Base64 PNG

Execution:

image_data = generate_image(
    prompt="Your detailed prompt here",
    resolution="2K",
    aspect_ratio="16:9",
    enable_google_search=True,  # For current info
    thinking_process=True        # Auto-optimize
)

Pattern 3: Image-to-Image Editing

Key Concept: Transform existing images with detailed instructions

Common Tasks:

  • Style Transfer: Convert to art style (Van Gogh, anime, etc.)
  • Object Manipulation: Add, remove, or modify elements
  • Composition Change: Reframe, zoom, or reposition subjects
  • Quality Enhancement: Upscale, improve detail, adjust colors

Flow:

  1. Load original image
  2. Write transformation instruction
  3. Reference images (optional)
  4. Apply edit maintaining coherence
  5. Retrieve edited image

Pattern 4: Multi-Turn Refinement

Key Concept: Iteratively improve images through conversation

Workflow:

  1. Generate initial image
  2. Review output
  3. Provide refinement instruction
  4. Regenerate with improvements
  5. Repeat (max 5 turns)

Example:

Turn 1: "A mountain landscape at sunset"
Turn 2: "Make the sky more dramatic with purple clouds"
Turn 3: "Add a lone tree in foreground"

Pattern 5: Reference Image Guidance

Key Concept: Use reference images to guide generation style

Supported References:

  • Up to 6 object references
  • Up to 5 human references
  • Style influences
  • Composition guides

Usage:

generate_image(
    prompt="Similar style to reference",
    reference_images=[
        "path/to/style_reference.png",
        "path/to/composition_ref.png"
    ]
)

📖 Advanced Documentation

This Skill uses Progressive Disclosure. For detailed implementation:


🎨 Model Selection Guide

Choose Nano Banana Pro when:

  • Professional quality required
  • 2K/4K resolution needed
  • Complex compositions
  • Sophisticated text in images
  • Real-time information important
  • Budget allows (higher cost)

Choose Gemini 2.5 Flash when:

  • Quick iterations needed
  • Prototyping and testing
  • High volume generation
  • 1K resolution sufficient
  • Speed critical
  • Cost sensitive

Quick Reference (30 seconds)

Core Purpose: Professional AI image generation using Nano Banana Pro (Gemini 3 Pro) and Gemini 2.5 Flash.

Key Features: Text-to-image, image-to-image editing, multi-turn refinement, reference guidance, 4K resolution.

When to Use: Visual asset creation, prototyping, documentation, UI mockups, marketing materials.


Works Well With

Agents:

  • design-uiux - UI/UX design integration
  • code-frontend - Frontend asset implementation
  • workflow-docs - Visual documentation generation

Skills:

  • moai-lang-unified - UI/UX implementation with generated assets
  • moai-docs-generation - Create visual documentation
  • moai-cc-claude-md - Embed generated images in markdown
  • moai-domain-frontend - Frontend integration

Commands:

  • /moai:3-sync - Documentation with visual assets
  • /moai:9-feedback - Image generation improvements

🔗 Integration with Other Skills

Typical Workflow:

  1. Use this Skill to generate visual assets
  2. Use moai-domain-frontend to implement in UI
  3. Use moai-docs-generation to document with images

📈 Version History

1.0.1 (2025-11-23)

  • 🔄 Refactored with Progressive Disclosure pattern
  • 📚 Detailed prompts moved to modules/
  • ✨ Core patterns highlighted in SKILL.md
  • ✨ Added model selection guide

1.0.0 (2025-11-12)

  • ✨ Nano Banana Pro (Gemini 3 Pro) support
  • ✨ Text-to-Image and Image-to-Image
  • ✨ Multi-turn refinement capability
  • ✨ Reference image guidance

Maintained by: alfred Domain: Image Generation & Visual Creation Generated with: MoAI-ADK Skill Factory

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

Claude Code

26.17%
按下载量换算20

OpenCode

24.79%
按下载量换算19

Antigravity

16.04%
按下载量换算13

Codex

12.26%
按下载量换算10

windsurf

6.82%
按下载量换算5

Gemini CLI

3.05%
按下载量换算2

安全审计

Gen Agent Trust Hub

可疑

Socket

通过

Snyk

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。

来源信息

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