crawl4ai

Extract web content from dynamic sites into clean markdown or structured records.

5|Updated Feb 8, 2026
One-click install
npx skills add https://github.com/tiancaiamao/ai --skill crawl4ai-tiancaiamao
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: crawl4ai
Source: https://github.com/tiancaiamao/ai/tree/main/skills/crawl4ai-skill
Command: npx skills add https://github.com/tiancaiamao/ai --skill crawl4ai-tiancaiamao

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires crawl4ai, packaging, beautifulsoup4, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Crawl4AI removes the manual effort of scraping websites, rendering JavaScript-heavy pages, and converting messy web content into clean markdown or structured records. It is built for repeatable extraction workflows where pages change often, pagination is common, and simple HTTP requests are not enough.

Core Features & Use Cases

  • Clean content capture: Turn articles, documentation, and search results into readable markdown with filtering for noise and low-value sections.
  • Structured extraction: Use CSS-based schemas for fast, deterministic data capture, or fall back to LLM extraction for irregular layouts.
  • Automation at scale: Process multiple URLs concurrently, reuse sessions for login flows, and handle proxies and anti-detection settings for tougher sites.
  • Common use cases: Product monitoring, news aggregation, documentation archiving, Google search scraping, and browser-based data pipelines.

Quick Start

Use the crawl4ai skill to scrape a target URL, extract the page into clean markdown, and return any structured fields you need in one pass.

Frequently Asked Questions about crawl4ai

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

FAQPage Schema
How do I scrape JavaScript-rendered web pages and get clean markdown?

Scraping JavaScript-rendered pages and extracting clean markdown is done by crawling dynamic websites, capturing fully rendered content, and filtering out noise to produce readable text suitable for documentation or articles.

What is the best way to extract structured data from web pages with irregular layouts?

Extracting structured data from irregular web layouts is handled by an LLM fallback extraction mechanism, which processes unstructured content when deterministic CSS-based schemas fail to capture the required target fields.

Can I process multiple URLs concurrently for batch crawling?

Yes, processing multiple URLs concurrently for batch crawling is supported. The skill handles concurrent crawling requirements, enabling automated web scraping at scale for product monitoring or news aggregation workflows.

Does crawl4ai support session persistence for login flows and proxy rotation?

Yes, crawl4ai supports session persistence for login flows and proxy management. You can reuse sessions to maintain authenticated states and configure proxies with anti-detection settings for scraping tougher sites.

How do I scrape Google search results and extract structured data?

Scraping Google search results and extracting structured data involves crawling search engine URLs, rendering JavaScript, and applying schema-based extraction to capture links, titles, and snippets into structured records.

What are the limitations of using CSS-based schemas for web scraping?

CSS-based schemas for web scraping are limited when handling irregular layouts or unstructured content, requiring an LLM fallback extraction approach to process pages where deterministic selectors cannot reliably capture target data.