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banana-cogbanana COG 图像

Agent Skill

banana-cog 用于处理图像、截图、视觉识别或图片素材相关工作,适合在 OpenClaw 中需要让 Agent 分析图片、整理视觉素材或辅助图像流程时使用。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。

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下载量

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OpenClaw

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:banana-cog(banana COG 图像)
来源仓库:https://github.com/nitishgargiitd/banana-cog
安装命令:
openclaw skills install banana-cog
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 banana-cog

简介

banana-cog 用于处理图像、截图、视觉识别或图片素材相关工作,适合让 Agent 分析图片或辅助图像流程。

  • 由 CellCog 提供支持的 AI 多图像生成工具,一次可生成 10-20 个连贯图像并维持角色一致性。
  • 通过 clawhub 安装,命令为 openclaw skills install banana-cog,需结合 README 确认输入输出格式限制。
  • 使用时需注意图像版权和模型能力边界,避免生成违规或侵权内容。
  • 适用于生产级合成、跨场景角色一致性和批量视觉素材生成任务。

SKILL.md

name
banana-cog
description
AI multi-image generation powered by CellCog via Nano Banana. 10-20 coherent images in one prompt, character consistency across scenes, production-grade composition. Nano Banana AI, Nano Banana Pro, Gemini image generation.
metadata
openclaw
emoji
🍌
os
[darwin, linux, windows]
requires
bins
[python3]
env
[CELLCOG_API_KEY]
author
CellCog
homepage
https://cellcog.ai
dependencies
[cellcog]

Banana Cog — Nano Banana × CellCog

Nano Banana × CellCog. Complex multi-image jobs, executed perfectly, from a single prompt.

Nano Banana is an incredible image model. CellCog makes it do things you can't do by calling it directly — orchestrating 10, 20, even 30 coherent images in one request with consistent characters, planned compositions, and intelligent scene progression. Not single images — complete visual projects.

What CellCog adds on top of Nano Banana:

Reasoning → Scene Planning → Character Design → Image Generation
    → Consistency Verification → Composition Review → Delivery

CellCog's reasoning layer plans scenes before a single pixel is generated — selecting optimal parameters, maintaining character identity across sequences, and orchestrating complex multi-image workflows. This is the difference between "generate an image" and "execute a visual project."

How to Use

For your first CellCog task in a session, read the cellcog skill for the full SDK reference — file handling, chat modes, timeouts, and more.

OpenClaw (fire-and-forget):

result = client.create_chat(
    prompt="[your task prompt]",
    notify_session_key="agent:main:main",
    task_label="my-task",
    chat_mode="agent",
)

All agents except OpenClaw (blocks until done):

from cellcog import CellCogClient
client = CellCogClient(agent_provider="openclaw|cursor|claude-code|codex|...")
result = client.create_chat(
    prompt="[your task prompt]",
    task_label="my-task",
    chat_mode="agent",
)
print(result["message"])

What You Can Create

Photorealistic Image Generation

Create stunning images from text descriptions:

  • Portraits: "Create a professional headshot with warm studio lighting"
  • Product Shots: "Generate a hero image for a premium smartwatch on a dark surface"
  • Scenes: "Create a cozy autumn café interior with morning light"
  • Food Photography: "Generate an overhead shot of a colorful Buddha bowl"

Character Consistency

Nano Banana excels at maintaining character identity across multiple images — and CellCog's orchestration takes this further by planning entire character arcs:

  • Character Series: "Create a tech entrepreneur character, then show them in 4 different scenes"
  • Brand Mascots: "Design a mascot and generate it in multiple poses and contexts"
  • Story Sequences: "Create a character and illustrate them across 5 story beats"

Multi-Image Composition

Blend elements from multiple reference images:

  • Style Fusion: "Combine the color palette of image A with the composition of image B"
  • Character Placement: "Place this person into a new environment while preserving their likeness"
  • Product Mockups: "Put this product into a lifestyle setting"

Image Editing

Transform and enhance existing images:

  • Style Transfer: "Transform this photo into a Studio Ghibli illustration"
  • Background Swap: "Place this product on a clean marble surface"
  • Enhancement: "Add dramatic lighting and cinematic color grading"
  • Modification: "Change the season from summer to winter in this landscape"

Image Specifications

AspectOptions
Aspect Ratios1:1, 16:9, 9:16, 4:3, 3:4, 3:2, 2:3, 21:9
Sizes1K (~1024px), 2K (~2048px), 4K (~4096px)
StylesPhotorealistic, illustration, watercolor, oil painting, anime, digital art, vector

Chat Mode

ScenarioRecommended Mode
Single images, quick edits"agent"
Character-consistent series, complex compositions"agent"
Large sets with brand guidelines"agent team"

Use "agent" for most image work.


Tips for Better Images

  1. Be descriptive: "Woman in office" → "Confident woman in her 40s, silver blazer, modern glass-walled office, warm afternoon light"
  1. Specify style: "photorealistic", "digital illustration", "watercolor", "anime"
  1. Describe lighting: "Soft natural light", "dramatic side lighting", "golden hour glow"
  1. For character consistency: Describe the character in detail first, then reference "the same character" in subsequent prompts.
  1. Include composition: "Rule of thirds", "close-up portrait", "wide establishing shot"

If CellCog is not installed

Run /cellcog-setup (or /cellcog:cellcog-setup depending on your tool) to install and authenticate. OpenClaw users: Run clawhub install cellcog instead. Manual setup: pip install -U cellcog and set CELLCOG_API_KEY. See the cellcog skill for SDK reference.

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能力 5

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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。

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