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web-scraping

Extract data from static and dynamic web pages using Python tools.

Updated Mar 20, 2026
Search Tags:#data extraction#web scraping#Python#Selenium#Scrapy
One-click install
npx skills add https://github.com/chestnut0698/BUPT-DS-Personalized-Tourism-System --skill web-scraping-chestnut0698
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: web-scraping
Source: https://github.com/chestnut0698/BUPT-DS-Personalized-Tourism-System/tree/main/.agents/skills/web-scraping
Command: npx skills add https://github.com/chestnut0698/BUPT-DS-Personalized-Tourism-System --skill web-scraping-chestnut0698

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, beautifulsoup4, lxml, selenium, scrapy, playwright, pyppeteer, agentQL, multion, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the process of data extraction from web pages, saving time and reducing manual effort.

Core Features & Use Cases

  • Static Sites: Extracts data from static HTML pages using requests, BeautifulSoup, and lxml.
  • Dynamic Content: Handles JavaScript-rendered pages with Selenium, Playwright, and pyppeteer.
  • Large-Scale Extraction: Supports structured crawling with Scrapy, AI-powered extraction with jina, and large-scale scraping with firecrawl.
  • Complex Workflows: Manages structured queries with agentQL and complex automation with multion.
  • Use Case: Automate the collection of product listings from e-commerce websites for market analysis.

Quick Start

Run the web-scraping script to extract data from the specified URL.

Frequently Asked Questions about web-scraping

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I extract data from dynamic JavaScript-rendered web pages?▼

To extract data from dynamic web pages, you can use Selenium, Playwright, or pyppeteer. These frameworks execute JavaScript, allowing Python scripts to capture the fully loaded HTML for scraping data that appears only after client-side execution.

What is the best way to scrape static HTML pages with Python?▼

The best way to scrape static HTML pages is using Python libraries like requests, BeautifulSoup, and lxml. This combination fetches the raw HTML content and parses the DOM tree efficiently to extract text and attributes without rendering overhead.

Can I use Scrapy for large-scale web scraping projects?▼

Yes, you can use Scrapy for large-scale web scraping projects. Scrapy supports structured crawling and asynchronous requests, making it suitable for extracting product listings and massive datasets across multiple pages efficiently.

Does Python web scraping handle complex automation workflows involving agentQL?▼

Python web scraping handles complex automation workflows using agentQL for structured queries and multion for complex task execution. This enables precise data extraction from intricate web interfaces without relying solely on traditional CSS selectors.

Why use firecrawl or jina for large-scale data extraction?▼

You use firecrawl for large-scale scraping and jina for AI-powered extraction to handle unstructured content efficiently. These tools bypass traditional parsing limitations by automatically processing complex layouts into clean, structured markdown or JSON data.

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