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image-generation图像生成

Agent Skill

用于辅助前端页面、组件、样式和交互逻辑的开发与维护。它适合让 Agent 生成或审查 React、Next.js、Vue、Tailwind、CSS 等相关代码,整理组件结构,或定位布局和性能问题。使用时需要结合项目现有设计系统、路由和构建方式,避免只生成孤立片段;涉及页面改动时,应配合本地预览和构建检查确认视觉效果。

总安装

384

周安装

16

GitHub Stars

4

下载量

128
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:image-generation(图像生成)
来源仓库:https://github.com/cleanexpo/ato
仓库路径:skills/image-generation
安装命令:
npx skills add https://github.com/cleanexpo/ato --skill image-generation
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/cleanexpo/ato --skill image-generation

简介

image-generation 提供 AI 驱动的视觉资产创建能力,支持产品图、营销图与演示素材生成。

  • 适用于内容创作、社交媒体运营或快速原型展示的视觉需求场景。
  • 基于 Imagen 3 模型生成分辨率最高 4K 的图片,涵盖写实、编辑与生活风格。
  • 使用前请确认内容合规性与品牌调性匹配,避免生成不当或侵权图像。
  • 建议将生成结果用于内部参考,正式发布前由人工复核质量与准确性。

SKILL.md

Image Generation Skill

AI-powered visual asset creation for content, marketing, and presentation workflows.

When to Use

Activate this skill when the task involves:

  • Creating product imagery or renders
  • Generating marketing visuals
  • Building infographics and diagrams
  • Creating presentation assets
  • Producing social media graphics

Capabilities

1. Photorealistic Renders

Generate high-fidelity product and scene imagery:

  • Model: Imagen 3 (Google)
  • Resolution: Up to 4K (4096x4096)
  • Styles: Photorealistic, studio, lifestyle, editorial

2. Marketing Visuals

Create branded marketing assets:

  • Social media graphics (1:1, 9:16, 16:9)
  • Banner ads and hero images
  • Email header graphics
  • Campaign keyart

3. Infographics

Generate data-driven visuals:

  • Process diagrams
  • Comparison charts
  • Timeline graphics
  • Icon sets

4. Creative Concepts

Produce artistic and conceptual imagery:

  • Abstract backgrounds
  • Artistic illustrations
  • Mood boards
  • Style explorations

Execution Pattern

1. BRIEF → Define visual requirements and context
2. PROMPT → Craft detailed generation prompt
3. GENERATE → Execute image generation request
4. REVIEW → Evaluate output quality and accuracy
5. REFINE → Iterate with enhanced prompts if needed
6. DELIVER → Export in required formats and sizes

Prompt Engineering

Structure

[Subject] + [Style] + [Composition] + [Lighting] + [Technical]

Example Prompts

Product Render:

Professional product photography of a modern steam cleaner,
studio lighting, white background, 45-degree angle,
high-key lighting, sharp focus, 8K resolution, commercial quality

Marketing Visual:

Dynamic hero image for cleaning services website,
professional cleaner in action, modern office environment,
shallow depth of field, warm natural lighting,
contemporary style, aspirational mood

Infographic Element:

Flat design icon set for cleaning industry,
minimalist style, consistent line weight,
cohesive color palette (blue, green, white),
vector-ready, transparent background

Output Format

<image_output>
  <metadata>
    <prompt>Original generation prompt</prompt>
    <model>imagen-3</model>
    <resolution>2048x2048</resolution>
    <seed>12345</seed>
  </metadata>

  <files>
    <file format="png" size="original" path="..." />
    <file format="webp" size="web-optimized" path="..." />
    <file format="jpg" size="thumbnail" path="..." />
  </files>

  <variations>
    <variation id="1" prompt_modifier="..." path="..." />
  </variations>
</image_output>

Aspect Ratios

RatioDimensionsUse Case
1:11024x1024Social media, thumbnails
4:31024x768Presentations
16:91920x1080Hero images, YouTube
9:161080x1920Stories, shorts
2:3800x1200Pinterest, posters

Integration Points

  • Google Slides Storyboard: Supplies presentation assets
  • Content Orchestrator: Receives asset generation requests
  • Social Commander: Provides social media graphics

Quality Guidelines

Do's

  • Use specific, detailed prompts
  • Include technical specifications
  • Request multiple variations
  • Specify lighting and composition

Don'ts

  • Avoid vague descriptions
  • Don't request copyrighted elements
  • Avoid text in images (use overlays instead)
  • Don't exceed model capabilities

Brand Consistency

When generating branded assets:

  1. Reference brand color codes (hex values)
  2. Specify typography style preferences
  3. Include brand element descriptions
  4. Maintain visual language consistency

Error Handling

ErrorRecovery
Generation failsSimplify prompt, retry
Low quality outputEnhance prompt specificity
Wrong styleAdd explicit style modifiers
Policy rejectionRephrase potentially flagged terms

Cost Considerations

  • Fuel Cost: 5-20 PTS per generation
  • Optimization:

- Batch similar requests - Cache reusable assets - Use lower resolution for drafts

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

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

25.17%
按下载量换算32

OpenCode

22.34%
按下载量换算29

Cursor

17.28%
按下载量换算22

Codex

12.58%
按下载量换算16

Antigravity

8.45%
按下载量换算11

Gemini CLI

3.29%
按下载量换算4

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。

来源信息

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