crawl4ai
Use when scraping JavaScript-heavy pages or SPAs, crawling multiple URLs concurrently, extracting structured data with reusable CSS/JSON schemas, or building automated web data pipelines. Wraps the Crawl4AI library (`crwl` CLI and Python SDK) with schema-generation patterns for LLM-free extraction. Triggers on crawl4ai, crwl, scrape JS-heavy site, scrape SPA, headless browser scrape, schema-based extraction, batch crawl, sitemap crawl, web data pipeline. SKIP when a static HTML page can be read with `defuddle` / `fetch-web` — those are faster cold-start and don't need a browser.
DeepseekModel
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v1.0.0
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name crawl4ai description Use when scraping JavaScript-heavy pages or SPAs, crawling multiple URLs concurrently, extracting structured data with reusable CSS/JSON schemas, or building automated web data pipelines. Wraps the Crawl4AI library (`crwl` CLI and Python SDK) with schema-generation patterns for LLM-free extraction. Triggers on crawl4ai, crwl, scrape JS-heavy site, scrape SPA, headless browser scrape, schema-based extraction, batch crawl, sitemap crawl, web data pipeline. SKIP when a static HTML page can be read with `defuddle` / `fetch-web` — those are faster cold-start and don't need a browser. argument-hint [url] Crawl4AI Verified against crawl4ai VERSION . PEP 723 pins in scripts/*.py and tests/*.py floor at that version. Overview Crawl4AI wraps a headless browser (Playwright) plus a markdown-aware content pipeline. Use it when defuddle/curl can't reach the content — JavaScript-rendered pages, login-gated content, infinite scroll, multi-URL concurrency, repeatable schema-based extraction. This skill exposes both interfaces of the underlying library: CLI ( crwl ) — quick, scriptable commands: CLI Guide Python SDK — full programmatic control: SDK Guide Invoked with a URL argument When the user runs /crawl4ai <url> with a single URL and no further qualifier, treat it as the JS-heavy fetch case and default to: crwl <url> -c "wait_until=networkidle,page_timeout=60000" -o markdown wait_until=networkidle waits for the network to be quiet for ~500ms post-load — the right default when the user hasn't named a specific element on a JS-rendered page. (Avoid wait_for=css:body : <body> exists at t=0 on every HTML response, so it's satisfied before JS renders content.) Then return the markdown to the agent context. Adjust to wait_for=css:<selector> if the user named a specific element. Skip the default and route to the relevant section below for any task that names extraction, batch / multi-URL, login / session, screenshot / PDF, or URL discovery — those each have their own pipeline. If the URL is clearly static (a docs page, a blog post), route the user to /fetch-web instead per the "When NOT to use" section below. When NOT to use this skill Static HTML pages (most documentation sites, blog posts, news articles, tweets) — use /fetch-web or defuddle directly. Static extraction is ~0ms cold start; crawl4ai pays a ~2s browser startup tax. Local file conversion ( .pdf , .docx , .pptx , .epub ) — use /markdown-convert . One-URL agent-context reads (the agent just needs to read this page) — use /fetch-web and let it route to defuddle . Mutating UI flows (form fills, multi-step clicks, login + navigation) — /browse (gstack's persistent headless Chromium) is built for that. When stuck For unknown crwl/SDK flags, scrape failures, or extraction edge cases the references don't cover, see references/escalation.md for the lookup order (qmd solutions → upstream docs → GitHub issues → ask the user) and worked examples. Quick Start Installation pip install crawl4ai crawl4ai-setup # Verify installation crawl4ai-doctor CLI (Recommended) # Basic crawling - returns markdown crwl https://example.com # Get markdown output crwl https://example.com -o markdown # JSON output with cache bypass crwl https://example.com -o json -v --bypass-cache # See more examples crwl --example Python SDK import asyncio from crawl4ai import AsyncWebCrawler async def main (): async with AsyncWebCrawler() as crawler: result = await crawler.arun( "https://example.com" ) print (result.markdown[: 500 ]) asyncio.run(main()) For SDK configuration details: SDK Guide - Configuration . Core Concepts Configuration Layers Both CLI and SDK use the same underlying configuration: Concept CLI SDK Browser settings -B browser.yml or -b "param=value" BrowserConfig(...) Crawl settings -C crawler.yml or -c "param=value" CrawlerRunConfig(...) Extraction -e extract.yml -s schema.json extraction_strategy=... Content filter -f filter.yml markdown_generator=... Key Parameters Browser Configuration: headless : Run with/without GUI viewport_width/height : Browser dimensions user_agent : Custom user agent proxy_config : Proxy settings Crawler Configuration: page_timeout : Max page load time (ms) wait_for : CSS selector or JS condition to wait for cache_mode : bypass, enabled, disabled js_code : JavaScript to execute css_selector : Focus on specific element For complete parameters: CLI Config | SDK Config Output Content Every crawl returns: markdown - Clean, formatted markdown html - Raw HTML links - Internal and external links discovered media - Images, videos, audio found extracted_content - Structured data (if extraction configured) Markdown Generation (Primary Use Case) Crawl4AI excels at generating clean, well-formatted markdown. CLI crwl https://docs.example.com -o markdown # raw markdown crwl https://docs.example.com -o markdown-fit # filtered (noise removed) crwl https://docs.example.com -f templates/filter_bm25.yml -o markdown-fit # BM25-relevance filter crwl https://docs.example.com -f templates/filter_pruning.yml -o markdown-fit # quality-based filter Filter templates: templates/filter_bm25.yml (relevance-scored against a query), templates/filter_pruning.yml (no query, prunes low-quality blocks). Python SDK from crawl4ai.content_filter_strategy import BM25ContentFilter from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator bm25_filter = BM25ContentFilter(user_query= "machine learning" , bm25_threshold= 1.0 ) md_generator = DefaultMarkdownGenerator(content_filter=bm25_filter) config = CrawlerRunConfig(markdown_generator=md_generator) result = await crawler.arun(url, config=config) print (result.markdown.fit_markdown) # Filtered print (result.markdown.raw_markdown) # Original For filter selection and config field reference, see Content Filters . Data Extraction 1. Schema-Based CSS Extraction (Most Efficient) No LLM required at extract time — fast, deterministic, cost-free. One-time LLM cost to derive the schema, then reuse indefinitely. The bundled scripts split the pipeline by responsibility: ./scripts/generate_schema.py https://shop.example.com "products with name, price, image" shop_schema.json ./scripts/extract_with_schema.py https://shop.example.com shop_schema.json products.json Or via the CLI with the YAML strategy template + the saved schema: crwl https://shop.example.com -e templates/extract_css.yml -s shop_schema.json -o json Schema skeleton: templates/css_schema.json . Strategy YAML: templates/extract_css.yml . 2. LLM-Based Extraction For one-off / irregular content where a CSS schema is too brittle: ./scripts/extract_with_llm.py https://news.example.com "Extract headlines, dates, summaries" news.json Or via the CLI with the strategy template: crwl https://news.example.com -e templates/extract_llm.yml -o json Strategy YAML: templates/extract_llm.yml . Pays an LLM call per URL — for repeat extraction, prefer the schema pipeline above. For extraction strategy reference: Extraction Strategies . Advanced Patterns Dynamic Content (JavaScript-Heavy Sites) crwl https://example.com -c "wait_for=css:.ajax-content,scan_full_page=true,page_timeout=60000" crwl https://example.com -C templates/crawler.yml # all options in a YAML file Crawler config template: templates/crawler.yml . Multi-URL Processing ./scripts/batch_crawl.py urls.txt --max-concurrent 5 --out batch_markdown/ ./scripts/batch_extract.py urls.txt shop_schema.json --max-concurrent 5 --out products.json The two scripts split on responsibility: batch_crawl.py returns markdown per URL; batch_extract.py returns schema-extracted JSON per URL. Python equivalent uses arun_many() : urls = [ "https://site1.com" , "https://site2.com" , "https://site3.com" ] results = await crawler.arun_many(urls, config=config) For batch processing reference: arun_many() Reference . URL Discovery Before Crawl When the URL list comes from a sitemap / domain rather than a known list, do discovery first, then feed the result into batch_crawl.py / batch_extract.py . See URL Discovery for the full surface; quick shape: from crawl4ai import AsyncUrlSeeder, SeedingConfig seeds = await AsyncUrlSeeder().urls( "example.com" , SeedingConfig( source= "sitemap+cc" , pattern= "*/blog/*" , query= "machine learning" , score_threshold= 0.3 , live_check= True , )) urls = [s[ "url" ] for s in seeds] AsyncUrlSeeder is best when you want BM25-scored filtering against a query; DomainMapper is best when you want maximum coverage of one domain. Session & Authentication Fill the login template, then reuse the session id on subsequent crawls: crwl https://site.com/login -C templates/login_crawler.yml crwl https://site.com/protected -c "session_id=user_session" Login template: templates/login_crawler.yml (fill in the field-id selectors and the post-login wait condition before use). For session management reference: Advanced Features . Anti-Detection & Proxies crwl https://example.com -B templates/browser.yml Browser config template: templates/browser.yml (uncomment proxy_config and init_scripts as needed). For pre-page-load script injection (fingerprint patches that must fire before any site script), populate init_scripts: rather than js_code: (which fires after the page loads). proxy_config works with both the browser strategy and the non-browser HTTPCrawlerStrategy — the latter is the cheap path for static fetches behind a corporate proxy. Full surface (CDP attachment, undetected mode, init script patterns): Anti-Detection . Rendering Cached HTML ( raw: / file:// ) If the agent already has HTML in hand (e.g., from defuddle or a previous crawl) and only needs a screenshot, PDF, or MHTML render, skip the network fetch and pass the HTML directly. base_url controls relative-link resolution: result = await crawler.arun( url= "raw:" + html_string, config=CrawlerRunConfig(base_url= "https://example.com" , screenshot= True , pdf= True ), ) result = await crawler.arun( url= "file:///path/to/page.html" , config=CrawlerRunConfig(screenshot= True ), ) Common Use Cases Eight worked end-to-end flows (docs page, JS-heavy SPA, e-commerce product extraction, news aggregation, topic-bound domain crawl, login-required content, render existing HTML, Q&A) live in Recipes . Pick the recipe closest to the task at hand and adapt. Resources Provided Scripts Script Responsibility scripts/basic_crawler.py <url> One URL → markdown + screenshot scripts/batch_crawl.py <urls.txt> Many URLs → markdown files scripts/batch_extract.py <urls.txt> <schema.json> Many URLs + schema → JSON
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| format | フォーマット識別子（skill/v1） |
| skill_id | スキル固有 ID |
| name | スキル名 |
| version | バージョン |
| description | 説明 |
| category | カテゴリ（配列） |
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