How do I install the Wepr Deepseek Crawler skill?

Run npx skillmds@latest add shenyuannext/wepr-deepseek-crawler in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Wepr Deepseek Crawler safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Wepr Deepseek Crawler?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Wepr Deepseek Crawler free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Wepr Deepseek Crawler?

ShenyuanNext (@shenyuannext) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Wepr Deepseek Crawler by shenyuannext

Name: Wepr Deepseek Crawler
Author: ShenyuanNext

Wepr Deepseek Crawler

当用户需要按关键词在 DeepSeek 网页端进行多轮独立窗口采样，并统计品牌、人物或产品的提及与引用情况时使用。输入包括关键词、采样次数、目标实体和实体类型；输出结构化 JSON 数据及可查看的 GEO HTML 报告。不用于普通网页抓取、DeepSeek API 对话、SEO 文案写作或一次性回答生成。

ShenyuanNext Updated Sep 17, 2026

File contents

WEPR DeepSeek 采样工具

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

Read references/user-setup-and-usage.md for install, prerequisites, and user-facing steps.
Read references/deepseek-crawl-workflow.md for crawler setup, preflight, delay, resume, and batch rules.
Read references/report-contract.md for JSON schema, metrics, target/competitor recognition, and report rules.
Run node scripts/preflight.mjs --profile <profile> before fresh crawling.
Stage 1: run scripts/deepseek_batch_crawl.mjs with questions, repeat, profile, target entity/type, --safe-random-delay, and output dir.
Stage 2: run scripts/analyze_deepseek_results.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.
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.

---name: wepr-deepseek-crawlerdescription: 当用户需要按关键词在 DeepSeek 网页端进行多轮独立窗口采样，并统计品牌、人物或产品的提及与引用情况时使用。输入包括关键词、采样次数、目标实体和实体类型；输出结构化 JSON 数据及可查看的 GEO HTML 报告。不用于普通网页抓取、DeepSeek API 对话、SEO 文案写作或一次性回答生成。---# WEPR DeepSeek 采样工具## InputsStandard 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.## Workflow1. 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/deepseek_batch_crawl.mjs` with questions, repeat, profile, target entity/type, `--safe-random-delay`, and output dir.6. Stage 2: run `scripts/analyze_deepseek_results.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.

Frequently asked questions