Crawl4AI provides comprehensive web crawling and data extraction capabilities. This skill supports both CLI (recommended for quick tasks) and Python SDK (for programmatic control).
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versioncrawl4aiExecute the skills CLI command in your project's root directory to begin installation:
Fetches crawl4ai from brettdavies/crawl4ai-skill and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate crawl4ai. Access via /crawl4ai in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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Crawl4AI provides comprehensive web crawling and data extraction capabilities. This skill supports both CLI (recommended for quick tasks) and Python SDK (for programmatic control).
Choose your interface:
crwl) - Quick, scriptable commands: CLI Guidepip install crawl4ai
crawl4ai-setup
# Verify installation
crawl4ai-doctor
# 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
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 (lines 61-150)
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=... |
Browser Configuration:
headless: Run with/without GUIviewport_width/height: Browser dimensionsuser_agent: Custom user agentproxy_config: Proxy settingsCrawler Configuration:
page_timeout: Max page load time (ms)wait_for: CSS selector or JS condition to wait forcache_mode: bypass, enabled, disabledjs_code: JavaScript to executecss_selector: Focus on specific elementFor complete parameters: CLI Config | SDK Config
Every crawl returns:
Crawl4AI excels at generating clean, well-formatted markdown:
# Basic markdown
crwl https://docs.example.com -o markdown
# Filtered markdown (removes noise)
crwl https://docs.example.com -o markdown-fit
# With content filter
crwl https://docs.example.com -f filter_bm25.yml -o markdown-fit
Filter configuration:
# filter_bm25.yml (relevance-based)
type: "bm25"
query: "machine learning tutorials"
threshold: 1.0
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 content filters: Content Processing (lines 2481-3101)
No LLM required - fast, deterministic, cost-free.
CLI:
# Generate schema once (uses LLM)
python scripts/extraction_pipeline.py --generate-schema https://shop.com "extract products"
# Use schema for extraction (no LLM)
crwl https://shop.com -e extract_css.yml -s product_schema.json -o json
Schema format:
{
"name": "products",
"baseSelector": ".product-card",
"fields": [
{"name": "title", "selector": "h2", "type": "text"},
{"name": "price", "selector": ".price", "type": "text"},
{"name": "link", "selector": "a", "type": "attribute", "attribute": "href"}
]
}
For complex or irregular content:
CLI:
# extract_llm.yml
type: "llm"
provider: "openai/gpt-4o-mini"
instruction: "Extract product names and prices"
api_token: "your-token"
crwl https://shop.com -e extract_llm.yml -o json
For extraction details: Extraction Strategies (lines 4522-5429)
CLI:
crwl https://example.com -c "wait_for=css:.ajax-content,scan_full_page=true,page_timeout=60000"
Crawler config:
# crawler.yml
wait_for: "css:.ajax-content"
scan_full_page: true
page_timeout: 60000
delay_before_return_html: 2.0
CLI (sequential):
for url in url1 url2 url3; do crwl "$url" -o markdown; done
Python SDK (concurrent):
urls = ["https://site1.com", "https://site2.com", "https://site3.com"]
results = await crawler.arun_many(urls, config=config)
For batch processing: arun_many() Reference (lines 1057-1224)
CLI:
# login_crawler.yml
session_id: "user_session"
js_code: |
document.querySelector('#username').value = 'user';
document.querySelector('#password').value = 'pass';
document.querySelector('#submit').click();
wait_for: "css:.dashboard"
# Login
crwl https://site.com/login -C login_crawler.yml
# Access protected content (session reused)
crwl https://site.com/protected -c "session_id=user_session"
For session management: Advanced Features (lines 5429-5940)
CLI:
# browser.yml
headless: true
proxy_config:
server: "http://proxy:8080"
username: "user"
password: "pass"
user_agent_mode: "random"
crwl https://example.com -B browser.yml
crwl https://docs.example.com -o markdown > docs.md
# Generate schema once
python scripts/extraction_pipeline.py --generate-schema https://shop.com "extract products"
# Monitor (no LLM costs)
crwl https://shop.com -e extract_css.yml -s schema.json -o json
# Multiple sources with filtering
for url inPrerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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crawl4ai is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
We added crawl4ai from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: crawl4ai is focused, and the summary matches what you get after install.
crawl4ai is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: crawl4ai is the kind of skill you can hand to a new teammate without a long onboarding doc.
crawl4ai reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend crawl4ai for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for crawl4ai matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in crawl4ai — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
crawl4ai fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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