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perfect-web-clone完美的网络克隆

Agent Skill

perfect-web-clone 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。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。

总安装

978

周安装

42

GitHub Stars

10

下载量

343
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:perfect-web-clone(完美的网络克隆)
来源仓库:https://github.com/ericshang98/perfect-web-clone-skill
仓库路径:skills/perfect-web-clone
安装命令:
npx skills add https://github.com/ericshang98/perfect-web-clone-skill --skill perfect-web-clone
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/ericshang98/perfect-web-clone-skill --skill perfect-web-clone

简介

用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中围绕仓库状态或代码变更进行整理。
  • 可结合来源仓库和原始 README 继续核验具体用法。
  • 安装前建议确认权限范围、维护状态及是否会触发联网或文件读写。
  • 注意避免对生产环境造成影响。

SKILL.md

Perfect Web Clone

Clone any webpage into pixel-perfect, production-ready code.

Quick Start

When a user provides a URL and asks to clone/replicate a webpage, follow this workflow:

Phase 1: Environment Setup

First, check if dependencies are installed:

python -c "from playwright.sync_api import sync_playwright; print('Playwright ready')" 2>/dev/null || echo "NEED_INSTALL"

If installation is needed, guide the user:

pip install playwright beautifulsoup4
playwright install chromium

Phase 2: Page Extraction

Run the extraction script to capture complete page data:

python scripts/extract_page.py "<URL>" --output page_data.json

This extracts 30+ data types including:

  • Complete DOM tree with computed styles
  • Full-page screenshot
  • CSS variables, animations, transitions
  • Theme detection (light/dark mode)
  • All images and assets

Phase 3: Intelligent Chunking

Run the chunking script following the Three Principles:

python scripts/chunk_content.py page_data.json --output chunks/ --max-tokens 50000

This produces individual JSON files for each section in chunks/ directory.

Phase 4: Parallel Code Generation

CRITICAL: Use the Task tool to spawn multiple subagents in parallel.

  1. Read all chunk files from chunks/ directory
  2. Determine parallelism based on user preference:

- User says "fastest" or "parallel" → spawn all agents simultaneously - User specifies a number → use that many parallel agents - Default → 3-5 parallel agents

  1. For each chunk, spawn a Task subagent with this prompt template:
You are a frontend developer focused on pixel-perfect replication.

## Your Task
Implement the [SECTION_NAME] section of a webpage clone.

## Input Data
- Section HTML: [FROM chunks/section_N.json → html field]
- Section Styles: [FROM chunks/section_N.json → styles field]
- Images: [FROM chunks/section_N.json → images field]
- Bounding Box: [FROM chunks/section_N.json → rect field]

## Requirements
1. **Pixel-Perfect**: Replicate the exact visual design
2. **Use Original URLs**: Keep all image src URLs as-is (user's localhost can access them directly)
3. **Tailwind CSS**: Use Tailwind for styling, inline styles only when necessary
4. **Self-Contained**: Component must work independently
5. **Responsive**: Implement reasonable breakpoints

## Output
Write a single React/Next.js component to: src/components/[SectionName].tsx
  1. Wait for all subagents to complete using TaskOutput

Phase 5: Project Assembly

After all sections are generated:

  1. Create the main page that imports all section components:
// src/app/page.tsx or src/pages/index.tsx
import Section1 from '@/components/Section1'
import Section2 from '@/components/Section2'
// ... import all sections

export default function Home() {
  return (
    <main>
      <Section1 />
      <Section2 />
      {/* ... all sections in order */}
    </main>
  )
}
  1. Ensure package.json has required dependencies (React, Next.js, Tailwind)
  2. Create tailwind.config.js if not exists
  3. Prompt user to run:
npm install && npm run dev

The Three Principles of Chunking

These principles ensure reliable, complete page replication:

Principle 1: Mutual Exclusivity

  • Rule: Chunks NEVER overlap
  • Implementation: Bounding box validation ensures no two chunks share DOM regions
  • Benefit: Each subagent works on isolated content with no conflicts

Principle 2: Complete Coverage

  • Rule: All chunks combined = entire page (no gaps)
  • Implementation: Gap detection fills any missing regions
  • Benefit: No part of the original page is lost

Principle 3: Size Control

  • Rule: Each chunk < 50,000 tokens
  • Implementation: Large sections are recursively split into children
  • Benefit: Each subagent receives manageable context

See docs/CHUNKING.md for detailed algorithm.


Parallel Configuration

Users can control parallelism with natural language:

User SaysBehavior
"clone this page"Default: 3 parallel agents
"clone with 5 parallel"Exactly 5 agents
"clone as fast as possible"All sections in parallel
"clone one by one"Sequential (1 agent)

Supported Tech Stacks

FrameworkStylingCommand
Next.js (default)Tailwind CSSnpx create-next-app
ReactTailwind CSSnpx create-react-app
Vue 3Tailwind CSSnpm create vue@latest

User can specify: "clone using Vue" or "clone with React"


Detailed Documentation


Troubleshooting

Playwright Installation Issues

# On macOS
brew install chromium
playwright install chromium

# On Linux
sudo apt-get install chromium-browser
playwright install chromium

Large Pages (100+ sections)

For very large pages, increase parallelism:

"Clone this page with maximum parallelism"

Dynamic Content Not Captured

The extractor scrolls the page to trigger lazy loading. For SPAs with complex loading:

python scripts/extract_page.py "<URL>" --wait 5000

About

Perfect Web Clone is an open-source Claude Code Skill created by Nexting.ai.

For a complete visual experience with:

  • Real-time preview in browser
  • Visual diff comparison
  • One-click deployment
  • Team collaboration

Visit nexting.ai to try our full-featured AI web development platform.


License

MIT License - Free for personal and commercial use.

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能力概览

能力 1

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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

34.25%
按下载量换算117

Claude

28.14%
按下载量换算97

Cursor

21%
按下载量换算72

Gemini CLI

9.38%
按下载量换算32

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

未通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

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 warning/failed 结果,不能写成本站确认安全。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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