by D4Vinci
🕷️ An adaptive Web Scraping framework that handles everything from a single request to a full-scale crawl!
# Add to your Claude Code skills
git clone https://github.com/D4Vinci/ScraplingGuides for using mcp servers skills like Scrapling.
Last scanned: 4/15/2026
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"status": "PASSED",
"scannedAt": "2026-04-15T06:03:17.306Z",
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}Scrapling is an open-source mcp servers skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by D4Vinci. 🕷️ An adaptive Web Scraping framework that handles everything from a single request to a full-scale crawl!. It has 69,067 GitHub stars.
Yes. Scrapling passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/D4Vinci/Scrapling" and add it to your Claude Code skills directory (see the Installation section above).
Scrapling is primarily written in Python. It is open-source under D4Vinci on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other MCP Servers skills you can browse and compare side by side. Open the MCP Servers category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh Scrapling against similar tools.
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Scrapling is an adaptive Web Scraping framework that handles everything from a single request to a full-scale crawl.
Its parser learns from website changes and automatically relocates your elements when pages update. Its fetchers bypass anti-bot systems like Cloudflare Turnstile out of the box. And its spider framework lets you scale up to concurrent, multi-session crawls with pause/resume and automatic proxy rotation - all in a few lines of Python. One library, zero compromises.
Blazing fast crawls with real-time stats and streaming. Built by Web Scrapers for Web Scrapers and regular users, there's something for everyone.
from scrapling.fetchers import Fetcher, AsyncFetcher, StealthyFetcher, DynamicFetcher
StealthyFetcher.adaptive = True
p = StealthyFetcher.fetch('https://example.com', headless=True, network_idle=True) # Fetch website under the radar!
products = p.css('.product', auto_save=True) # Scrape data that survives website design changes!
products = p.css('.product', adaptive=True) # Later, if the website structure changes, pass `adaptive=True` to find them!
Or scale up to full crawls
from scrapling.spiders import Spider, Response
class MySpider(Spider):
name = "demo"
start_urls = ["https://example.com/"]
async def parse(self, response: Response):
for item in response.css('.product'):
yield {"title": item.css('h2::text').get()}
MySpider().start()