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geniusdapeng-collabgeniusdapeng-collab/hyperreality-system/skills/registry/scrapling-collector/SKILL.md

自适应数据采集框架(Scrapling 采集面)

Skill10 starsChanged 55 days agoApache-2.0View on GitHub

用 Scrapling 做批量公开数据采集——自适应元素定位(页面改版自动重定位)、爬虫模板(CrawlSpider/SitemapSpider 等)、断点恢复、自动限速、结构化导出。适用于竞品监控、比价、内容采集等高频批量场景。

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SKILL.md

自适应数据采集框架(Scrapling 采集面)

触发(何时用)

  • 批量数据采集:要从一批页面/整个站点抓取结构化数据(价格、房源、商品、文章),不是操作单个页面时;
  • 目标站结构会变动:选择器经常失效、需要"记一次、自动跟着页面改版走"的自适应定位时;
  • 需要现成爬虫范式:整站爬取(CrawlSpider)、站点地图(SitemapSpider)、Feed/Shopify 采集,不想从零写循环时;
  • 需要后台 API 数据:页面数据其实来自 XHR/fetch 接口,用 capture_xhr 直接收响应比解析 DOM 更快时;
  • 需要结构化交付:结果直接导出 JSON/JSONL/CSV/XML 进下游流程时。

何时不用它(分工纪律):

场景用谁
单次页面操作(点击/填表/截图/登录态操作)computer-use 或 browser-playwright 技能
固定流程固化(每天同样的点击序列)browser-playwright 技能
多账号矩阵、强反封锁对抗、Cloudflare 付费级防护browser-act 技能(可选,须审批)
采集公开页面数据、结构变动频繁的站点本技能

步骤

  1. 判断抓取层级:静态页用 Fetcher(纯 HTTP,最快);动态渲染用 DynamicFetcher(Playwright Chromium);有拦截用 StealthyFetcher(指纹伪装)。能用浅的就不用深的;
  2. 记住关键元素:首次解析用 auto_save=True 保存元素特征,后续调用传 adaptive=True——页面改版后按相似度自动重新定位,不再因选择器失效返工;
  3. 批量任务用 Spider:start_urls + parse 声明式写法;开 AutoThrottle(按目标站响应自动调速,被限速按 Retry-After 退避);大任务开断点保存(Ctrl+C 可恢复);
  4. 接口数据直接收:数据来自后台 API 时用 capture_xhr 收 XHR 响应,跳过 DOM 解析;
  5. 导出进下游:内置 JSON/JSONL/CSV/XML 导出,直接对接 WorkData 或文件交付;
  6. 多会话:不同目标/不同反爬级别用会话路由(sid)分流,代理经 ProxyRotator 轮换(客户自带代理)。

边界(什么不做)

  • 只采公开数据:目标站须为公开页面或客户已获授权;目标站清单进围栏审批(fenceParams.targetDomainsRequireApproval),未批准的目标不采;
  • 尊重 robots.txt:respect_robots_txt=True 为默认;关闭须审批并留痕;
  • 不硬闯反爬:StealthyFetcher 的指纹伪装用于正常采集防误伤;solve_cloudflare 级对抗默认关闭,开启须审批(与 browser-act 同纪律);Akamai/DataDome 级企业防护不硬碰,转人工评估;
  • 限速是天条:AutoThrottle 常开;单目标站日请求上限由 fenceParams.dailyActionCap 约束,默认 500 起;
  • 登录态由用户本人完成,API Key 与登录态只存本机,不进技能正文、不出站;
  • 采到的数据按 WorkData 三段瀑布处理(PII 脱敏后才可进上下文或出站)。

安装与依赖

pip install "scrapling[fetchers]"   # Python ≥ 3.10
scrapling install                    # 下载浏览器依赖(DynamicFetcher 用)

MCP 服务器形态(scrapling[ai])为可选增强,默认不启用;启用须审批并声明出站。

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