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yao-doubao-crawler

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Use when a user needs repeated Doubao AI-search collection from web or Android Appium into compatible JSON plus Markdown/Excel/HTML GEO reports. Requires keywords/questions and repeat count; target entity/type are required only for target-vs-competitor diagnosis. Not for generic scraping, Doubao API chat, hidden API extraction, SEO writing, high-frequency automation, or one-off answers.

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Source SKILL.md: https://github.com/yaojingang/yao-geo-skills/blob/HEAD/skills/yao-doubao-crawler/SKILL.md

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Yao Doubao Crawler

Inputs

Collect with keywords/questions, repeat count, backend (web or mobile), connection fields, and output dir. Add target entity + entity type for standard diagnosis. Without a target, generate exploratory output and label inferred candidates as heuristic.

Workflow

  1. Read references/user-setup-and-usage.md, references/doubao-crawl-workflow.md, and references/report-contract.md.
  2. For Android/Appium, also read references/mobile-appium-workflow.md and run python3 scripts/doubao_mobile_crawl.py preflight ....
  3. Stage 1 web: run scripts/doubao_batch_crawl.mjs.
  4. Stage 1 mobile: run scripts/doubao_mobile_crawl.py batch; use --fresh-chat --require-fresh-chat when samples must start in separate Doubao app conversations.
  5. Stage 2: run scripts/analyze_doubao_results.py on the compatible crawl JSON.
  6. Use evals/ and templates/ for packaging checks and collection briefs.
  7. Return crawl JSON, summary.json, structured Markdown/Excel, HTML report, screenshots/XML for mobile, failed logs, and mobile search-material evidence when present.

Honest Boundaries

  • Do not bypass login, CAPTCHA, risk controls, hidden APIs, network traffic, or app storage.
  • Android/Appium is low-frequency visible UI evidence capture.
  • Probability and competitor metrics are repeated-sample estimates; review aliases before external use.
  • Preserve raw answers, references, URLs, screenshots/XML, and logs.