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图像处理敏感数据github未标认证来源可访问许可证需确认审计通过

cover-image封面图片

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。

总安装

1,224

周安装

51

GitHub Stars

334

下载量

408
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

2

许可证

unknown

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:cover-image(封面图片)
来源仓库:https://github.com/notedit/happy-skills
仓库路径:skills/cover-image
安装命令:
npx skills add https://github.com/notedit/happy-skills --skill cover-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 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/notedit/happy-skills --skill cover-image

简介

用于辅助图像生成和图片编辑,适合根据文本生成图片和处理视觉素材。

  • 适用于视觉提示词整理和图像模型调用场景,可处理背景优化需求。
  • 使用时需确认输入图片和输出格式,涉及人物或品牌时核对授权合规要求。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装,需确认图像生成权限。
  • cover-image 属于图像处理类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Cover Image Generator

Generate elegant cover images for articles with 5-dimensional customization. Supports Chinese text rendering.

Usage

# Auto-select dimensions based on content
/cover-image path/to/article.md

# Quick mode: skip confirmation
/cover-image article.md --quick

# Specify dimensions
/cover-image article.md --type conceptual --palette warm --rendering flat-vector

# WeChat Official Account cover
/cover-image article.md --aspect 2.35:1

# Specify provider
/cover-image article.md --provider qwen

# Use style preset
/cover-image article.md --style tech-dark

# Large font for better visibility
/cover-image article.md --font-size large

# Generate multiple options
/cover-image article.md --n 3

# With reference images (Google/OpenAI only)
/cover-image article.md --ref style-ref.png

# Direct content input
/cover-image --palette mono --aspect 1:1 --quick
[paste content]

Options

OptionDescription
--type <name>hero, conceptual, typography, metaphor, scene, minimal
--palette <name>warm, elegant, cool, dark, earth, vivid, pastel, mono, retro
--rendering <name>flat-vector, hand-drawn, painterly, digital, pixel, chalk
--text <level>none, title-only, title-subtitle, text-rich
--mood <level>subtle, balanced, bold
--font <name>clean, handwritten, serif, display
--font-size <size>small, medium (default), large, xlarge
--aspect <ratio>16:9 (default), 2.35:1 (公众号封面), 4:3, 3:2, 1:1, 3:4
--provider <name>qwen (default), openai, google
--style <preset>tech-dark, tech-clean, lifestyle-warm, business-elegant, announcement-bold, minimal-zen, creative-playful, retro-vintage
--lang <code>Title language (en, zh, ja, etc.)
--no-titleAlias for --text none
--n <count>Number of images to generate (1-4)
--quickSkip confirmation, use auto-selection
--ref <files...>Reference images for style/composition guidance

Providers

ProviderModelText RenderingChinese SupportPrice
qwenqwen-image-plusExcellentNative Chinese0.2 CNY/image
openaigpt-image-1GoodLimited~$0.04/image
googlegemini-2.0-flash-expGoodLimitedFree tier available

Provider Selection Logic

  1. --provider specified -> use it
  2. Chinese title detected -> auto-select qwen
  3. Reference images provided -> use google or openai
  4. Default -> qwen

Qwen-Image (Default)

Best for Chinese text rendering. Native support for Chinese characters.

API Endpoint: https://dashscope.aliyuncs.com/api/v1/services/aigc/text2image/image-synthesis

Environment Variable: DASHSCOPE_API_KEY

Supported Sizes: 1664*928, 1024*1024, 928*1664, 1472*1104, 1104*1472

OpenAI

Good for English text and creative designs.

Environment Variable: OPENAI_API_KEY

Supported Sizes: 1024x1024, 1536x1024, 1024x1536

Google

Good for multimodal generation with reference images.

Environment Variable: GOOGLE_API_KEY

Supported Sizes: Various aspect ratios supported

Five Dimensions

DimensionValuesDefault
Typehero, conceptual, typography, metaphor, scene, minimalauto
Palettewarm, elegant, cool, dark, earth, vivid, pastel, mono, retroauto
Renderingflat-vector, hand-drawn, painterly, digital, pixel, chalkauto
Textnone, title-only, title-subtitle, text-richtitle-only
Moodsubtle, balanced, boldbalanced
Fontclean, handwritten, serif, displayclean

Auto-selection rules: references/auto-selection.md

Galleries

Types: hero, conceptual, typography, metaphor, scene, minimal -> Details: references/types.md

Palettes: warm, elegant, cool, dark, earth, vivid, pastel, mono, retro -> Details: references/palettes/

Renderings: flat-vector, hand-drawn, painterly, digital, pixel, chalk -> Details: references/renderings/

Text Levels: none (pure visual) | title-only (default) | title-subtitle | text-rich (with tags) -> Details: references/dimensions/text.md

Mood Levels: subtle (low contrast) | balanced (default) | bold (high contrast) -> Details: references/dimensions/mood.md

Fonts: clean (sans-serif) | handwritten | serif | display (bold decorative) -> Details: references/dimensions/font.md

File Structure

Output directory per default_output_dir preference:

  • same-dir: {article-dir}/
  • imgs-subdir: {article-dir}/imgs/
  • independent (default): cover-image/{topic-slug}/
<output-dir>/
├── source-{slug}.{ext}    # Source files
├── refs/                  # Reference images (if provided)
│   ├── ref-01-{slug}.{ext}
│   └── ref-01-{slug}.md   # Description file
├── prompts/cover.md       # Generation prompt
└── cover.png              # Output image

Slug: 2-4 words, kebab-case. Conflict: append -YYYYMMDD-HHMMSS

Workflow

Progress Checklist

Cover Image Progress:
- [ ] Step 1: Analyze content + save refs + determine output dir
- [ ] Step 2: Confirm options (6 dimensions + provider) unless --quick
- [ ] Step 3: Create prompt
- [ ] Step 4: Generate image
- [ ] Step 5: Resize for platform (if needed)
- [ ] Step 6: Completion report

Step 1: Analyze Content

  1. Save reference images (if provided) -> references/workflow/reference-images.md
  2. Save source content (if pasted, save to source.md)
  3. Analyze content: topic, tone, keywords, visual metaphors
  4. Deep analyze references: Extract specific, concrete elements
  5. Detect language: Compare source, user input
  6. Determine output directory: Per File Structure rules
  7. Auto-select provider: Based on language and references

Step 2: Confirm Options

Full confirmation flow: references/workflow/confirm-options.md

ConditionSkippedStill Asked
--quick6 dimensions + providerAspect ratio (unless --aspect)
All 6 + --aspect + --provider specifiedAllNone

Step 3: Create Prompt

Save to prompts/cover.md. Template: references/workflow/prompt-template.md

CRITICAL - References in Frontmatter:

  • Files saved to refs/ -> Add to frontmatter references list
  • Style extracted verbally (no file) -> Omit references, describe in body
  • Before writing -> Verify: test -f refs/ref-NN-{slug}.{ext}

Reference elements in body MUST be detailed, prefixed with "MUST"/"REQUIRED", with integration approach.

Step 4: Generate Image

  1. Backup existing cover.png if regenerating
  2. Select provider based on language and options
  3. Process references from prompt frontmatter:

- direct usage -> pass reference images directly (google/openai only) - style/palette -> extract traits, append to prompt

  1. Generate via provider API:

- Qwen: POST to dashscope API, poll for result - OpenAI: Use ImageGen tool or API - Google: Use ImageGen tool with reference images

  1. On failure: auto-retry once

Step 5: Resize for Platform

Resize generated image to target platform size:

PlatformTarget SizeAspect Ratio
公众号封面900x3832.35:1
博客/视频1920x108016:9
小红书1080x10801:1
手机海报1080x14403:4

Step 6: Completion Report

Cover Generated!

Provider: [provider]
Topic: [topic]
Type: [type] | Palette: [palette] | Rendering: [rendering]
Text: [text] | Mood: [mood] | Font: [font] | Aspect: [ratio]
Title: [title or "visual only"]
Language: [lang]
References: [N images or "extracted style" or "none"]
Location: [directory path]

Files:
- source-{slug}.{ext}
- prompts/cover.md
- cover.png
- cover-{platform}.png (if resized)

Composition Principles

  • Whitespace: 40-60% breathing room
  • Visual anchor: Main element centered or offset left
  • Characters: Simplified silhouettes; NO realistic humans
  • Title: Use exact title from user/source; never invent

Aspect Ratios

RatioPlatformUse Case
2.35:1微信公众号封面公众号首图,900x383px
16:9通用宽屏博客、视频封面
1:1社交媒体微博、小红书、Instagram
4:3经典比例演示文稿、传统媒体
3:2摄影标准照片风格封面
3:4竖版手机端、海报

Environment Variables

VariableRequired ForGet From
DASHSCOPE_API_KEYQwen (default)https://bailian.console.aliyun.com
OPENAI_API_KEYOpenAIhttps://platform.openai.com
GOOGLE_API_KEYGooglehttps://aistudio.google.com

References

Dimensions: text.md | mood.md | font.md Palettes: references/palettes/ Renderings: references/renderings/ Types: references/types.md Style Presets: references/style-presets.md Auto-Selection: references/auto-selection.md Visual Elements: references/visual-elements.md Workflow: confirm-options.md | prompt-template.md | reference-images.md | qwen-api.md

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

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

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

Codex

35.2%
按下载量换算144

Claude

32.79%
按下载量换算134

Cursor

18.59%
按下载量换算76

Gemini CLI

10.96%
按下载量换算45

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

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

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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