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Agent Skill · 图像处理

image-generation(图像生成)

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。

image-generation 是图像处理类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描有未通过项,按安装量在图像处理类的 1,562 个 Skill 里排第 153。

总安装 22,209周安装 954GitHub Stars 64,272下载 7,785核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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/bytedance/deer-flow
仓库路径:skills/image-generation
安装命令:
npx skills add https://github.com/bytedance/deer-flow --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。该命令会通过 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/bytedance/deer-flow --skill image-generation

这个 Skill 是做什么的

图像处理类 Skill 处理生成、裁剪、压缩和批量转换图片的任务。

用于辅助图像生成、图片编辑和视觉素材处理,适合根据文本生成图片或调用相关图像工具。适用于 Codex、Claude、Cursor、Gemini CLI 等宿主环境,支持结构化提示词和参考图片输入。通过 JSON 格式化提示词并执行 Python 脚本生成图像,需确认版权来源和输出格式限制。

安装方式:github,命令为 npx skills add https://github.com/bytedance/deer-flow --skill image-generation。涉及人物、品牌或公开展示素材时,应核对授权、真实性和内容合规边界。

来源仓库内路径:skills/image-generation

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/bytedance/deer-flow --skill image-generation;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

image-generation 怎么安装?

在终端执行 npx skills add https://github.com/bytedance/deer-flow --skill image-generation。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

image-generation 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

image-generation 需要什么权限?

权限级别是需要联网。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。

image-generation 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

image-generation 用的人多吗?

公开数据是累计安装 22,209 次,近一周安装 954 次,下载 7,785 次,GitHub 64,272 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

image-generation 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 来源仓库有 64,272 星,维护活跃度相对可查。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理图像处理类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

图像处理类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
ai-image-generation

AI 图像生成 · Codex、Claude

4,365,552382调用外部服务未明确有提醒
openai-image-gen

OpenAI 图像生成 · OpenClaw

1,906,88432需要联网MIT-0有提醒
image

图像 · OpenClaw

525,99926需要联网MIT-0通过
baoyu-cover-image

宝玉封面图 · Codex、Claude

474,24016,779只读MIT有提醒
baoyu-image-gen

宝玉图像生成器 · Codex、Claude

468,00716,778接触敏感数据MIT通过
image-generation

图像生成 · 本页

22,20964,272需要联网未明确未通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Image Generation Skill

Overview

This skill generates high-quality images using structured prompts and a Python script. The workflow includes creating JSON-formatted prompts and executing image generation with optional reference images.

Core Capabilities

  • Create structured JSON prompts for AIGC image generation
  • Support multiple reference images for style/composition guidance
  • Generate images through automated Python script execution
  • Handle various image generation scenarios (character design, scenes, products, etc.)

Workflow

Step 1: Understand Requirements

When a user requests image generation, identify:

  • Subject/content: What should be in the image
  • Style preferences: Art style, mood, color palette
  • Technical specs: Aspect ratio, composition, lighting
  • Reference images: Any images to guide generation
  • You don't need to check the folder under /mnt/user-data

Step 2: Create Structured Prompt

Generate a structured JSON file in /mnt/user-data/workspace/ with naming pattern: {descriptive-name}.json

Step 3: Execute Generation

Call the Python script:

python /mnt/skills/public/image-generation/scripts/generate.py \
  --prompt-file /mnt/user-data/workspace/prompt-file.json \
  --reference-images /path/to/ref1.jpg /path/to/ref2.png \
  --output-file /mnt/user-data/outputs/generated-image.jpg
  --aspect-ratio 16:9

Parameters:

  • --prompt-file: Absolute path to JSON prompt file (required)
  • --reference-images: Absolute paths to reference images (optional, space-separated)
  • --output-file: Absolute path to output image file (required)
  • --aspect-ratio: Aspect ratio of the generated image (optional, default: 16:9)

[!NOTE] Do NOT read the python file, just call it with the parameters.

Character Generation Example

User request: "Create a Tokyo street style woman character in 1990s"

Create prompt file: /mnt/user-data/workspace/asian-woman.json

{
  "characters": [{
    "gender": "female",
    "age": "mid-20s",
    "ethnicity": "Japanese",
    "body_type": "slender, elegant",
    "facial_features": "delicate features, expressive eyes, subtle makeup with emphasis on lips, long dark hair partially wet from rain",
    "clothing": "stylish trench coat, designer handbag, high heels, contemporary Tokyo street fashion",
    "accessories": "minimal jewelry, statement earrings, leather handbag",
    "era": "1990s"
  }],
  "negative_prompt": "blurry face, deformed, low quality, overly sharp digital look, oversaturated colors, artificial lighting, studio setting, posed, selfie angle",
  "style": "Leica M11 street photography aesthetic, film-like rendering, natural color palette with slight warmth, bokeh background blur, analog photography feel",
  "composition": "medium shot, rule of thirds, subject slightly off-center, environmental context of Tokyo street visible, shallow depth of field isolating subject",
  "lighting": "neon lights from signs and storefronts, wet pavement reflections, soft ambient city glow, natural street lighting, rim lighting from background neons",
  "color_palette": "muted naturalistic tones, warm skin tones, cool blue and magenta neon accents, desaturated compared to digital photography, film grain texture"
}

Execute generation:

python /mnt/skills/public/image-generation/scripts/generate.py \
  --prompt-file /mnt/user-data/workspace/cyberpunk-hacker.json \
  --output-file /mnt/user-data/outputs/cyberpunk-hacker-01.jpg \
  --aspect-ratio 2:3

With reference images:

{
  "characters": [{
    "gender": "based on [Image 1]",
    "age": "based on [Image 1]",
    "ethnicity": "human from [Image 1] adapted to Star Wars universe",
    "body_type": "based on [Image 1]",
    "facial_features": "matching [Image 1] with slight weathered look from space travel",
    "clothing": "Star Wars style outfit - worn leather jacket with utility vest, cargo pants with tactical pouches, scuffed boots, belt with holster",
    "accessories": "blaster pistol on hip, comlink device on wrist, goggles pushed up on forehead, satchel with supplies, personal vehicle based on [Image 2]",
    "era": "Star Wars universe, post-Empire era"
  }],
  "prompt": "Character inspired by [Image 1] standing next to a vehicle inspired by [Image 2] on a bustling alien planet street in Star Wars universe aesthetic. Character wearing worn leather jacket with utility vest, cargo pants with tactical pouches, scuffed boots, belt with blaster holster. The vehicle adapted to Star Wars aesthetic with weathered metal panels, repulsor engines, desert dust covering, parked on the street. Exotic alien marketplace street with multi-level architecture, weathered metal structures, hanging market stalls with colorful awnings, alien species walking by as background characters. Twin suns casting warm golden light, atmospheric dust particles in air, moisture vaporators visible in distance. Gritty lived-in Star Wars aesthetic, practical effects look, film grain texture, cinematic composition.",
  "negative_prompt": "clean futuristic look, sterile environment, overly CGI appearance, fantasy medieval elements, Earth architecture, modern city",
  "style": "Star Wars original trilogy aesthetic, lived-in universe, practical effects inspired, cinematic film look, slightly desaturated with warm tones",
  "composition": "medium wide shot, character in foreground with alien street extending into background, environmental storytelling, rule of thirds",
  "lighting": "warm golden hour lighting from twin suns, rim lighting on character, atmospheric haze, practical light sources from market stalls",
  "color_palette": "warm sandy tones, ochre and sienna, dusty blues, weathered metals, muted earth colors with pops of alien market colors",
  "technical": {
    "aspect_ratio": "9:16",
    "quality": "high",
    "detail_level": "highly detailed with film-like texture"
  }
}
python /mnt/skills/public/image-generation/scripts/generate.py \
  --prompt-file /mnt/user-data/workspace/star-wars-scene.json \
  --reference-images /mnt/user-data/uploads/character-ref.jpg /mnt/user-data/uploads/vehicle-ref.jpg \
  --output-file /mnt/user-data/outputs/star-wars-scene-01.jpg \
  --aspect-ratio 16:9

Common Scenarios

Use different JSON schemas for different scenarios.

Character Design:

  • Physical attributes (gender, age, ethnicity, body type)
  • Facial features and expressions
  • Clothing and accessories
  • Historical era or setting
  • Pose and context

Scene Generation:

  • Environment description
  • Time of day, weather
  • Mood and atmosphere
  • Focal points and composition

Product Visualization:

  • Product details and materials
  • Lighting setup
  • Background and context
  • Presentation angle

Specific Templates

Read the following template file only when matching the user request.

Output Handling

After generation:

  • Images are typically saved in /mnt/user-data/outputs/
  • Share generated images with user using present_files tool
  • Provide brief description of the generation result
  • Offer to iterate if adjustments needed

Tips: Enhancing Generation with Reference Images

For scenarios where visual accuracy is critical, use the image_search tool first to find reference images before generation.

Recommended scenarios for using image_search tool:

  • Character/Portrait Generation: Search for similar poses, expressions, or styles to guide facial features and body proportions
  • Specific Objects or Products: Find reference images of real objects to ensure accurate representation
  • Architectural or Environmental Scenes: Search for location references to capture authentic details
  • Fashion and Clothing: Find style references to ensure accurate garment details and styling

Example workflow:

  1. Call the image_search tool to find suitable reference images: image_search(query="Japanese woman street photography 1990s", size="Large")
  2. Download the returned image URLs to local files
  3. Use the downloaded images as --reference-images parameter in the generation script

This approach significantly improves generation quality by providing the model with concrete visual guidance rather than relying solely on text descriptions.

Notes

  • Always use English for prompts regardless of user's language
  • JSON format ensures structured, parsable prompts
  • Reference images enhance generation quality significantly
  • Iterative refinement is normal for optimal results
  • For character generation, include the detailed character object plus a consolidated prompt field

平台分布

Codex

36.83%
按下载量换算2,867

Claude

27.58%
按下载量换算2,147

Cursor

18.24%
按下载量换算1,420

Gemini CLI

10.48%
按下载量换算816

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

未通过

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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