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crawl4ai

AI-powered web scraping framework for extracting structured data from websites. Use when Codex needs to crawl, scrape, or extract data from web pages using AI-powered parsing, handle dynamic content, or work with complex HTML structures.

codylrn8040 来源安装数☆ 0 仓库 Stars

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。

SkillHub CLI

skillhub install codylrn804/crawl4ai

打开来源仓库,查看说明和兼容环境后,按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent,让它先读取说明、核对依赖,并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

Crawl4ai

Overview

Crawl4ai is an AI-powered web scraping framework designed to extract structured data from websites efficiently. It combines traditional HTML parsing with AI to handle dynamic content, extract text intelligently, and clean and structure data from complex web pages.

When to Use This Skill

Use when Codex needs to:

  • Extract structured data from web pages (products, articles, forms, tables, etc.)
  • Scrape websites with dynamic content or complex JavaScript
  • Clean and normalize extracted data from various HTML structures
  • Work with APIs or web services that return HTML
  • Handle CORS limitations by scraping directly
  • Process web content at scale with reliability

Trigger phrases:

  • "Extract data from this website"
  • "Scrape this page for [specific data]"
  • "Parse this HTML"
  • "Get data from [URL]"
  • "Extract structured information from [website]"
  • "Scrape [website] for [data type]"
  • "Web scrape [URL]"

Quick Start

Basic Usage
from crawl4ai import AsyncWebCrawler, BrowserMode

async def scrape_page(url):
    async with AsyncWebCrawler() as crawler:
        result = await crawler.arun(
            url=url,
            browser_mode=BrowserMode.LATEST,
            headless=True
        )
        return result.markdown, result.clean_html
Extracting Structured Data
from crawl4ai import AsyncWebCrawler, JsonModeScreener
import json

async def extract_products(url):
    async with AsyncWebCrawler() as crawler:
        result = await crawler.arun(
            url=url,
            screenshot=True,
            javascript=True,
            bypass_cache=True
        )
        # Extract product data
        products = []
        for item in result.extracted_content:
            if item['type'] == 'product':
                products.append({
                    'name': item['name'],
                    'price': item['price'],
                    'url': item['url']
                })
        return products
```...

来源信息

Source
github
Repository
openclaw/skills
Path
skills/codylrn804/crawl4ai
Version
—
Commit
30db0f76104c2ad310bd4a72ff17f107eb751133
分类来源
rules
状态
active
可见性
public

这些信息来自收录时的来源分析,可能滞后;使用前请核对最新来源文件。

来源内容

SKILL.md
# Crawl4ai

## Overview

Crawl4ai is an AI-powered web scraping framework designed to extract structured data from websites efficiently. It combines traditional HTML parsing with AI to handle dynamic content, extract text intelligently, and clean and structure data from complex web pages.

## When to Use This Skill

Use when Codex needs to:
- Extract structured data from web pages (products, articles, forms, tables, etc.)
- Scrape websites with dynamic content or complex JavaScript
- Clean and normalize extracted data from various HTML structures
- Work with APIs or web services that return HTML
- Handle CORS limitations by scraping directly
- Process web content at scale with reliability

**Trigger phrases:**
- "Extract data from this website"
- "Scrape this page for [specific data]"
- "Parse this HTML"
- "Get data from [URL]"
- "Extract structured information from [website]"
- "Scrape [website] for [data type]"
- "Web scrape [URL]"

## Quick Start

### Basic Usage

```python
from crawl4ai import AsyncWebCrawler, BrowserMode

async def scrape_page(url):
    async with AsyncWebCrawler() as crawler:
        result = await crawler.arun(
            url=url,
            browser_mode=BrowserMode.LATEST,
            headless=True
        )
        return result.markdown, result.clean_html
```

### Extracting Structured Data

```python
from crawl4ai import AsyncWebCrawler, JsonModeScreener
import json

async def extract_products(url):
    async with AsyncWebCrawler() as crawler:
        result = await crawler.arun(
            url=url,
            screenshot=True,
            javascript=True,
            bypass_cache=True
        )
        # Extract product data
        products = []
        for item in result.extracted_content:
            if item['type'] == 'product':
                products.append({
                    'name': item['name'],
                    'price': item['price'],
                    'url': item['url']
                })
        return products
```...

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