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yaojingang/yao-geo-skills

yao-deepseek-crawler

Use when a user provides DeepSeek web AI-search keywords, repeat count, target entity, and entity type, then needs repeated fresh-window crawls aggregated into JSON plus a Kami HTML GEO report.

First seen Jun 30, 2026

Installation

$ npx skills add yaojingang/yao-geo-skills --skill yao-deepseek-crawler
View on skills.sh ↗ GitHub repository ↗

Summary

  • Use when a user provides DeepSeek web AI-search keywords, repeat count, target entity, and entity type, then needs repeated fresh-window crawls aggregated into JSON plus a Kami HTML GEO report.
  • Not for generic website crawling, DeepSeek API chat, SEO writing, or one-off answer generation.

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Also in this package

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npx skills add yaojingang/yao-geo-skills
yao-geo-tracking 22 yao-geoflow-cli 21 yao-geoflow-template 20 yao-geoflow-design 20 yao-geo-panorama-audit 20 yao-geo-page-audit 20 yao-geo-knowledge-base-builder 20 yao-geo-intent-miner 20 yao-geo-page-blueprint 19 yao-geo-execution-roadmap 19

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 736
License LICENSE
Default branch main
Open issues 0
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 2,506 B
  • docs README.md 9,226 B
  • docs SUMMARY.md 317 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 10 installs

SKILL.md

Yao DeepSeek Crawler

Inputs

Standard inputs: keywords/questions, repeat count, target entity, entity type (人/person, 公司/company, 产品/product), browser profile, and optional output directory. Competitors must match the target type. Reports default to Simplified Chinese with an English summary toggle.

Workflow

  1. Read references/user-setup-and-usage.md for install, prerequisites, and user-facing steps.
  2. Read references/deepseek-crawl-workflow.md for crawler setup, preflight, delay, resume, and batch rules.
  3. Read references/report-contract.md for JSON schema, metrics, target/competitor recognition, and report rules.
  4. Run node scripts/preflight.mjs --profile <profile> before fresh crawling.
  5. Stage 1: run scripts/deepseekbatchcrawl.mjs with questions, repeat, profile, target entity/type, --safe-random-delay, and output dir.
  6. Stage 2: run scripts/analyzedeepseekresults.py on any crawl JSON with target entity/type, optional brands file, report output dir, and semantic review mode. Use --semantic-review auto by default; use --semantic-review required for formal delivery when AI review must pass.
  7. Return the raw crawl JSON, structured Markdown, structured Excel workbook, HTML report, summary JSON, semantic-review cache when present, and failed logs. Reports include AI semantic labels for entity recognition, target-vs-best-3 radar, click-to-reveal bubbles, Chinese source names, clickable citations, title intent, compact treemap, and GEO actions.

Honest Boundaries

  • Do not use for generic website crawling, DeepSeek API chat, SEO copywriting, or one-off answer generation.
  • Reuses local DeepSeek web automation; does not bypass login, CAPTCHA, bot checks, or hidden data.
  • Probability metrics are repeated-sample estimates, not ground truth.
  • Inferred competitors are heuristic unless --semantic-review required passes. AI semantic review is an audit enhancement and never replaces hard-rule gates or answer-body evidence.
  • Review aliases, semantic labels, excluded candidates, and competitor tables before external use.
  • Preserve raw answers, reference titles, URLs, and logs.