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图像处理只读github未标认证来源可访问许可证需确认审计通过

xhs-imagesxhs 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

324

周安装

13

GitHub Stars

34

下载量

105
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:xhs-images(xhs 图片)
来源仓库:https://github.com/manwithshit/xhs-images
仓库路径:skills/xhs-images
安装命令:
npx skills add https://github.com/manwithshit/xhs-images --skill xhs-images
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/manwithshit/xhs-images --skill xhs-images

简介

用于辅助图像生成、图片编辑和视觉素材处理。xhs-images 属于图像处理类 Skill,可作为该场景下的辅助能力补充。

  • 适合根据文本生成图片、处理背景或整理视觉提示词。
  • 使用时需确认输入图片、版权来源和模型限制。
  • 涉及人物、品牌或公开展示素材时应核对授权和合规边界。
  • 安装前建议确认权限范围和维护状态,以及是否会触发文件读写或网络请求。

SKILL.md

Xiaohongshu Infographic Series Generator

Break down complex content into eye-catching infographic series for Xiaohongshu.

Usage

# Auto-select style and layout based on content
/xhs-images posts/ai-future/article.md

# Specify style
/xhs-images posts/ai-future/article.md --style notion

# Specify layout
/xhs-images posts/ai-future/article.md --layout dense

# Combine style and layout
/xhs-images posts/ai-future/article.md --style tech --layout list

# Direct content input
/xhs-images
[paste content]

Options

OptionDescription
--style <name>Visual style (see Style Gallery below)
--layout <name>Information layout (see Layout Gallery below)

Two Dimensions

DimensionControlsOptions
StyleVisual aesthetics: colors, lines, decorationscute, fresh, tech, warm, bold, minimal, retro, pop, notion, productivity, insight
LayoutInformation structure: density, arrangementsparse, balanced, dense, list, comparison, flow

Style × Layout can be freely combined. Example: --style notion --layout dense creates an intellectual-looking knowledge card with high information density.

Style Gallery (Quick Reference)

StyleDescriptionBest For
cuteSweet, adorable, girly - classic XHS aestheticLifestyle, beauty, fashion
freshClean, refreshing, naturalHealth, wellness, self-care
techModern, smart, digitalTech tutorials, AI content
warmCozy, friendly, approachablePersonal stories, life lessons
boldHigh impact, attention-grabbingImportant tips, warnings
minimalUltra-clean, sophisticatedProfessional content
retroVintage, nostalgic, trendyThrowback, classic tips
popVibrant, energetic, eye-catchingFun facts, announcements
notionMinimalist hand-drawn line artKnowledge sharing, SaaS
productivityStructured, light mode, clean UIHow-to tutorials, tools
insightHigh clarity, dark mode, premiumMental models, deep thoughts

For detailed style specs (colors, elements, typography): See references/styles.md

Layout Gallery (Quick Reference)

LayoutDensityBest For
sparse1-2 points, 60-70% whitespaceCovers, quotes, impactful statements
balanced3-4 points, 40-50% whitespaceRegular content, tutorials
dense5-8 points, 20-30% whitespaceSummary cards, cheat sheets
list4-7 items, 30-40% whitespaceTop N lists, checklists
comparison2×2-4 points, 30-40% whitespaceBefore/After, pros/cons
flow3-6 steps, 30-40% whitespaceProcesses, timelines

For detailed layout specs and Style×Layout matrix: See references/layouts.md

Auto Selection Logic

Auto Style Selection

Content SignalsSelected Style
Beauty, fashion, cute, girl, pinkcute
Health, nature, clean, freshfresh
Tech, AI, code, digital, app, tooltech
Life, story, emotion, feelingwarm
Warning, important, must, criticalbold
Professional, business, elegantminimal
Classic, vintage, old, traditionalretro
Fun, exciting, wow, amazingpop
Knowledge, concept, productivity, SaaSnotion
How-to, tutorial, tool recommendationproductivity
Mental model, deep thought, insightinsight

Auto Layout Selection

Content SignalsSelected Layout
Single quote, one key point, coversparse
3-4 points, explanation, tutorialbalanced
5+ points, summary, cheat sheetdense
Numbered items, top N, checklistlist
vs, compare, before/after, pros/conscomparison
Process, flow, timeline, ordered stepsflow

Layout by Position

PositionRecommended Layout
Coversparse
Contentbalanced / dense / list (content-appropriate)
Endingsparse or balanced

File Management

With Article Path

Save to xhs-images/ subdirectory in the same folder as the article:

posts/ai-future/
├── article.md
└── xhs-images/
    ├── outline.md
    ├── prompts/
    │   ├── 01-cover.md
    │   └── ...
    ├── 01-cover.png
    └── 02-ending.png

Without Article Path

Save to xhs-outputs/YYYY-MM-DD/[topic-slug]/

Workflow

Step 1: Analyze Content & Select Style/Layout

  1. Read content
  2. If --style specified, use that style; otherwise auto-select
  3. If --layout specified, use that layout; otherwise auto-select per image
  4. Determine image count:

- Simple topic: 2-3 images - Medium complexity: 4-6 images - Deep dive: 7-10 images

Step 2: Generate Outline

Plan for each image with style and layout specifications. Save as outline.md:

# Xiaohongshu Infographic Series Outline

**Topic**: [topic]
**Style**: [selected style]
**Default Layout**: [selected layout or "varies"]
**Image Count**: N
**Generated**: YYYY-MM-DD HH:mm

---

## Image 1 of N

**Position**: Cover
**Layout**: sparse
**Core Message**: [one-liner]
**Filename**: 01-cover.png

**Text Content**:
- Title: xxx
- Subtitle: xxx

**Visual Concept**: [style + layout appropriate description]

---
...

Step 3: Generate Images One by One

For each image:

  1. Read style details from references/styles.md (load target style section only)
  2. Read layout details from references/layouts.md (load target layout section only)
  3. Create prompt file in prompts/ directory
  4. Generate using:
/gemini-web --promptfiles [SKILL_ROOT]/prompts/system.md [TARGET_DIR]/prompts/01-cover.md --image [TARGET_DIR]/01-cover.png

Prompt Format:

Infographic theme: [topic]
Style: [style name]
Layout: [layout name]
Position: [cover/content/ending]

Visual composition:
- Main visual: [style-appropriate description]
- Arrangement: [layout-specific structure]
- Decorative elements: [style-specific decorations]

Color scheme:
- Primary: [from style spec]
- Background: [from style spec]
- Accent: [from style spec]

Text content:
- Title: 「xxx」(large, prominent)
- Key points: [based on layout density]

Layout instructions: [from layout spec]
Style notes: [from style spec]

Step 4: Completion Report

Xiaohongshu Infographic Series Complete!

Topic: [topic]
Style: [style name]
Layout: [layout name or "varies"]
Location: [directory path]
Images: N total

- 01-cover.png ✓ Cover (sparse)
- 02-content-1.png ✓ Content (balanced)
- 03-ending.png ✓ Ending (sparse)

Outline: outline.md

Content Breakdown Principles

  1. Cover (Image 1): Strong visual impact, core title, attention hook → sparse layout
  2. Content (Middle): Core points per image, density varies by content
  3. Ending (Last): Summary / call-to-action / memorable quote → sparse or balanced

Notes

  • Image generation typically takes 10-30 seconds per image
  • Auto-retry once on generation failure
  • Use cartoon alternatives for sensitive public figures
  • Output language matches input content language
  • Maintain selected style consistency across all images in series

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

36.39%
按下载量换算38

Claude

30.6%
按下载量换算32

Cursor

20%
按下载量换算21

Gemini CLI

10.04%
按下载量换算11

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。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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