BC cn-data-scraper
Chinese website data scraping expert with anti-bypass strategies (中国网站数据爬取专家+反爬绕过策略). Teach AI agents to scrape Chinese websites that Scrapling alone can't handle — Baidu anti-crawl, Taobao login walls, Douyin dynamic rendering, Zhihu verification, WeChat articles, 1688 product data. Features: (1) Platform-specific anti-crawl bypass recipes for 10+ Chinese websites, (2) Scrapling integration guides with Chinese site configurations, (3) Adaptive selector strategies for frequently-redesigned Chinese sites, (4) Legal compliance boundary guide (what's legal vs illegal in China's data scraping), (5) Executable scripts for common scraping tasks, (6) MCP server integration for AI agent workflows. ONLY skill combining Chinese website scraping expertise + legal compliance + Scrapling integration. Use when: scraping Chinese websites, bypassing Baidu anti-crawl, scraping Taobao data, Douyin data extraction, Zhihu scraping, WeChat article scraping, 1688 product scraping, Chinese data collection, 爬虫, 数据爬取, 反爬绕过, 百度反爬, 淘宝爬虫, 抖音数据, 知乎爬虫, 微信文章抓取, Scrapling Chinese, 中国网站爬虫. Triggers: Chinese web scraping, data scraping China, anti-crawl bypass, Baidu scraping, Taobao scraping, Douyin scraping, Zhihu scraping, WeChat scraping, 1688 scraping, 爬虫工具, 数据采集, 反爬虫, 信息差, Scrapling配置, 中国网站数据, cn-scraping, web scraping China, data extraction Chinese websites, crawler Chinese sites, Python爬虫中国网站
Chinese website data scraping expert with anti-bypass strategies (中国网站数据爬取专家+反爬绕过策略).
As a process C 53/100 · Has gaps — weak spots: result and completion, inputs and preconditions, progress reporting
How to improve
- Shorten the description to 1024 characters.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- error
description-longdescription is 1389 chars, limit 1024 - warning
body-longSKILL.md body ≈ 5951 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 53/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 50When it triggers. No condition that starts the skill
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70Execution cost. Instruction body is 5951 tokens
- 100Steps. 117 steps
- 100Consistency. Name and required fields are in place
- 100Running it twice. No mutating operations
- low 12 top-level sections: this looks like several domains in one skill
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 1389: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- -240 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +4Structure: 35 headings
- +3Step-by-step instructions: 117 items
- +4Has examples (19 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 47.