A specialized scraping and automation skill featuring platform-specific bypass configs and legal compliance recipes for major Chinese websites using Scrapling.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install cn-data-scraper
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install cn-data-scraper using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
Chinese Data Scraper is an essential package in the Openclaw Skills library, designed to bridge the gap between generic web crawling frameworks and the highly sophisticated anti-scraping ecosystems of major Chinese platforms. While general tools solve standard crawling tasks, the Chinese web ecosystem presents distinct hurdles, including multi-layer cookie verification, mandatory login gates, and rapid UI redesigns. This skill equips developer agents with robust, platform-specific bypass configurations for top-tier sites like Baidu, Taobao, Douyin, Zhihu, WeChat, 1688, Xiaohongshu, Weibo, CSDN, and Boss Zhipin.
By incorporating this skill into your AI workflows via Openclaw Skills, you unlock not only high-speed, adaptive selector code snippets but also a comprehensive legal compliance framework. Developers and AI agents can analyze the ethical boundaries of their scraping plans under Chinese laws like the Personal Information Protection Law (PIPL) and Data Security Law, ensuring data collection remains efficient, highly optimized, and legally compliant.
Install the Scrapling framework with all required headless browser engine integrations:
# Install Scrapling with all features
pip install scrapling[all]
# Or execute a minimal installation
pip install scrapling
Create a basic scraper using a custom platform recipe from Openclaw Skills:
from scrapling import StealthyFetcher
# Baidu search with anti-crawl bypass
page = StealthyFetcher.fetch(
'https://www.baidu.com/s?wd=Openclaw',
headless=True,
network_idle=True,
timeout=30000,
)
# Extract search results using structural selectors
results = page.css('div.c-container')
for result in results:
title = result.css_first('h3 a')
snippet = result.css_first('[class*="content"]')
if title:
print(f"Title: {title.text()}")
The skill provides templates to standardize data extraction models across different niches. Below are the key schemas provided for standard integrations:
| Field | Description | Target Platforms | Selector Strategy |
|---|---|---|---|
title |
Name of the listed item | Taobao, 1688, JD | span.Title--titleSpan / a.title |
price |
Integer price listed on page | Taobao, 1688, JD | span.Price--priceInt / span.price |
sales / min_order |
Total units sold or wholesale minimums | Taobao, 1688 | span.Sales--sales / span.min-order |
shop / supplier |
Official merchant or company name | Taobao, 1688 | a.ShopName--shopName / a.company-name |
| Field | Description | Target Platforms | Selector Strategy |
|---|---|---|---|
author |
Screen name of the publisher | Weibo, Xiaohongshu, Douyin | a.name / span.name / span.author-card-user-name |
content |
Body text or title description | Weibo, Xiaohongshu, Douyin | p.txt / span.note-text / p.title |
likes |
Direct like / reaction count | Weibo, Xiaohongshu, Douyin | Structural element count / .like-wrapper .count |
comments |
Total comment count | Weibo, Xiaohongshu, Douyin | a[action-type="flcomment"] em / CSS fallback |
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