deeptutor

name	deeptutor
description	Comprehensive academic advisor investigation and evaluation system for graduate school decisions. Investigates professors at any institution worldwide — searches publications (PubMed/Scopus/Scholar/OpenAlex), maps co-author networks, tracks student trajectories (the #1 predictive signal), classifies advisor type, and assesses exploitation/toxicity risk. Automatically applies region-specific search strategies: Chinese institutions (mainland China) use 知乎/小木虫/百度学术/CNKI; international institutions use Reddit/RateMyProfessors/ GradCafe/LinkedIn. Outputs a standalone .html report in the user's language — Chinese input produces Chinese report, English input produces English report, and so on for any language. Use this skill whenever evaluating a professor as a potential graduate advisor — including when users say "调查导师", "评估教授", "选导师", "导师怎么样", "能不能跟这个老师读研", "这个导师push吗", "investigate this advisor", "should I join this lab", "evaluate professor", "is this prof good", "rate my potential advisor", "review this PI", or provide a professor's name + institution for evaluation. Also triggers on comparative requests ("帮我对比这三个导师", "compare these advisors").
metadata	{"author":"jiadizhu","version":"1.3","license":"MIT"}

DeepTutor v5 — Academic Advisor Investigation System

Core Principle

Your ceiling = your seniors' ceiling.

Student outcomes are the single most predictive signal for advisor quality. A professor with stellar publications but whose students consistently end up in unclear positions is a red flag. A professor with modest metrics but whose students thrive is gold. Always weight student trajectory evidence above all other dimensions.

Language & Region Detection

Language Rule

Respond in whatever language the user writes in. If the user writes in Chinese, the entire report — titles, analysis, recommendations — must be in Chinese. If in English, everything in English. If in Japanese, Korean, or any other language, follow that language throughout. Never mix languages within a report unless quoting original source text.

Region Detection

Determine region from the institution name. This affects which search platforms to use and which evaluation criteria apply.

Region	Institutions	Strategy
Mainland China	Any university/institute in 中国大陆	Chinese strategy → references/chinese_academic_system.md
International	US, EU, UK, Japan, Korea, Australia, Singapore, etc.	International strategy → references/international_academic_system.md
Hong Kong / Macau / Taiwan	HKU, CUHK, HKUST, NTU, NTHU, etc.	Hybrid — use both Chinese social platforms AND international academic platforms

When uncertain about region, ask the user.

Input Requirements

Minimum input: Professor name + institution name.

If the user hasn't provided these, ask:

1. Career goal (shapes the Goal-Advisor Match scoring dimension)
   • Chinese context: 读博深造 / 考公考编 / 进大厂 / 药企CRO / 进医院 / 纯拿学位
   • International context: Academic career (tenure-track) / Industry R&D / Consulting & Finance / Government & Policy / Startup / Just get degree
2. Risk tolerance: Conservative / Moderate / Aggressive
3. Specific concerns (optional): e.g., "I heard the lab has high turnover"

If the user doesn't provide career goal or risk tolerance, proceed with a balanced evaluation and note that the Goal-Match dimension couldn't be fully scored.

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Model Capability Detection & Version Selection

DeepTutor has two investigation modes. The right mode depends on the model running it.

Auto-Detection Rule

Full Version (完整版) — run without asking:

• Claude Opus 4.6+, Claude Sonnet 4.6+, Codex series, and future Claude models of equivalent or higher capability

Prompt user to choose — for all other models (GPT-4o, Gemini, GLM, MiniMax, Haiku, etc.), display:

⚠️ DeepTutor 模式选择 检测到当前模型非 Opus/Sonnet 4.6 级别。

• 完整版: 10阶段/11维度/18节报告（推荐高端模型）
• 轻量版: 6阶段/7维度/7节报告（Token约完整版40%，可能遗漏部分信息） 请选择：完整版 or 轻量版？

Lite Version: 6-Phase Workflow

If the user chooses Lite, read references/lite_mode.md for the full specification. Key differences:

• 6 phases (skip co-author network, funding analysis, macro trend deep dive, retirement risk)
• 7 scoring dimensions (merge and drop 4 dimensions, re-weight)
• Simplified Sharp Critique (5-line template instead of 7-question framework)
• 7-section report (instead of 18)

Report Generation (Both Versions)

Both Full and Lite versions should output structured JSON and use scripts/generate_report.py for HTML rendering:

# Model outputs investigation data as JSON → script renders HTML
python scripts/generate_report.py report_data.json -o report.html

This separates investigation (model's job) from rendering (script's job). Even Full version benefits from this — the model focuses on analysis, not wrestling with CSS.

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10-Phase Investigation Workflow (Full Version)

Phase 1: Identity Resolution

Establish the professor's verified identity across platforms. This prevents investigating the wrong person (especially common with Chinese names that have many romanization variants).

For all regions:

• Official faculty page (university website)
• Google Scholar profile
• Scopus Author ID / ORCID
• Semantic Scholar

Chinese-specific additions:

• Baidu Scholar (百度学术)
• ResearchGate
• X-MOL faculty profile
• NSFC funded project database (kd.nsfc.cn)
• ScholarMate

International-specific additions:

• DBLP (for CS)
• Web of Science ResearcherID
• Personal/lab website
• GitHub (for computational fields)

Key verification: Cross-reference at least 3 platforms. Confirm institution, department, research area, and photo (if available) all align. For Chinese scholars, generate ALL name romanization variants — see references/publication_search_protocol.md for the template.

Phase 2: Student Trajectory Tracking (THE MOST IMPORTANT PHASE)

This phase implements the "ceiling principle." Track as many current and former students as possible.

How to find students:

• Lab/group website "Members" or "Alumni" page
• Co-authored papers (students are typically first authors)
• University thesis/dissertation databases
• Chinese: CNKI/万方 thesis search, 小木虫 lab discussions
• International: LinkedIn (search "[professor name] lab" or "[university] [department]"), ProQuest Dissertations, university digital repositories

What to track for each student:

Field	Description
Name	Student's name
Period	Years in the lab (start–end)
Degree	Master's / PhD / Postdoc
First-author papers	Count and quality (journal tier)
Current position	Where they are now
Time to degree	Normal or extended?

Ceiling/Floor analysis:

• High ceiling: Multiple students in tenure-track faculty, top-tier postdocs, or leadership roles in industry
• Mid ceiling: Students in decent positions but not exceptional
• Low ceiling: Students in unclear/untraceable positions, frequent attrition
• Red flag: Cannot find ANY student outcomes — either very new PI or students don't want to be associated

Phase 3: Publication Analysis

Follow the protocol in references/publication_search_protocol.md EXACTLY. The mandatory rule: always start with a BROAD search (no field keywords), then narrow down.

Search sequence:

1. Broad PubMed/Scopus/Scholar search with name + institution (NO topic keywords)
2. Author ID-anchored search (Scopus ID, ORCID, Semantic Scholar)
3. All name variants from the romanization template
4. Cross-database verification (minimum 3 databases)

Analyze:

• Total output, h-index, i10-index
• Publication trend (increasing/stable/declining)
• Journal quality distribution (top-tier / mid-tier / low-tier)
• Student first-authorship ratio
• Publication gaps (use the 6-step verification checklist before concluding any gap)
• Preprint activity (bioRxiv, arXiv, medRxiv)

Phase 4: Co-Author Network & Advisor Classification

Build a co-author frequency table from the publication record. Classify relationships:

• Internal collaborators (same institution)
• External academic collaborators
• Clinical/industry collaborators
• Student/postdoc co-authors

Advisor Type Classification:

Type	Chinese Label	Description	Key Signal
Research-Focused	学术型	Deep academic focus, pushes for top publications	Students publish well but may face high pressure
Grant/Project-Driven	项目型	Funded by applied/industry projects	Students may do project work instead of thesis research
Semi-Independent	半放养型	Gives moderate guidance, allows flexibility	Good for self-motivated students
Mentorship-Heavy	指导型	Hands-on guidance, frequent meetings	Great for students needing structure
Hands-Off	纯放养型	Minimal guidance, students largely on their own	Good if you have clear goals; risky otherwise

Classify based on: meeting frequency, student authorship patterns, project types (basic vs applied), student independence signals.

Phase 5: Funding Analysis

Chinese institutions → Read references/chinese_academic_system.md:

• NSFC grants (青年/面上/重点/杰青/优青)
• Ministry-level programs (973, National Key R&D)
• Provincial and university internal grants
• Industry/hospital collaboration (横向) funding

International institutions → Read references/international_academic_system.md:

• Government grants (NIH R01/R21, NSF CAREER, ERC Starting/Consolidator/Advanced, EPSRC, DFG, JSPS)
• Foundation grants (HHMI, Wellcome Trust, Gates Foundation)
• Industry funding and consulting
• Startup funds (common for new faculty)

Assess:

• Continuous vs sporadic funding
• Funding trajectory (growing or shrinking)
• Diversity of funding sources
• Whether funding supports student stipends and research

Phase 6: Contextual Intelligence — Social & Reputation Search

This phase uses region-specific platforms to gather student reviews and lab culture signals.

Chinese Mainland Strategy

Search these platforms for: "导师名" + 评价/怎么样/读研/课题组/实验室/push/pua

Platform	URL Pattern	What to Find
知乎	zhihu.com	Lab culture, student experiences, detailed reviews
小木虫	emuch.net	Grad student discussions, lab reputation
保研论坛	baoyan.net	Recommendation letters, interview experiences
小红书	xiaohongshu.com	Recent student experiences (newer platform)
百度贴吧	tieba.baidu.com	University-specific discussions
考研帮	kaoyan.com	Exam and advisor selection discussions

Also search: university BBS, WeChat public accounts (if accessible), news articles about the professor.

International Strategy

Search these platforms for: "professor name" + "university" + review/advisor/lab/experience/toxic

Platform	URL Pattern	What to Find
Reddit	r/GradSchool, r/AskAcademia, r/PhD, field-specific subs	Lab culture, warnings, experiences
RateMyProfessors	ratemyprofessors.com	Teaching quality (proxy for mentoring style)
GradCafe	thegradcafe.com	Admission discussions, lab reputation
Glassdoor	glassdoor.com	For industry-adjacent labs, postdoc reviews
Twitter/X	x.com	Academic community discussions, controversies
LinkedIn	linkedin.com	Student trajectory, lab alumni network
Quora	quora.com	Occasional advisor reviews

Also search: department-specific student surveys (some universities publish these), news articles, academic misconduct databases (Retraction Watch).

Hong Kong / Macau / Taiwan Strategy

Combine BOTH Chinese and international platforms, plus:

• PTT (Taiwan: ptt.cc)
• LIHKG (Hong Kong: lihkg.com)
• Dcard (Taiwan/HK student platform)
• 小红书 and 知乎 (many HK/TW students post here)

Phase 6.5: Field Macro Trend Analysis (行业宏观趋势判断)

方向不对，再好的导师也帮不了你。

在完成社会评价搜索后、打分之前，必须对导师所在研究领域进行宏观趋势判断。这不是简单的"hotspot or not"，而是系统性地评估这个领域对学生未来5-10年职业发展的影响。

必须回答的5个核心问题：

1. 生命周期定位：这个领域处于什么阶段？

   • 🌱 萌芽期（Emerging）：新技术/新概念，论文少但增长快，风险高回报高
   • 📈 上升期（Growth）：资金涌入，招聘旺盛，竞争加剧但机会多
   • 📊 成熟期（Mature）：方法论稳定，工业化应用，增量创新为主
   • 📉 衰退期（Declining）：资金缩减，人才外流，被新技术替代
   • ☠️ 夕阳期（Sunset）：几乎无新资金，从业者转行，学生就业极难

2. 资金趋势：近5年该领域的国家级基金（NSFC/NIH/ERC）资助数量和金额是增是减？有没有新的专项计划？

3. 就业市场前景：

   • 学术界：该领域的faculty招聘岗位是否在增加？
   • 工业界：对口企业/岗位有哪些？薪资水平？招聘趋势？
   • 医疗/政府：是否有对口的临床或政策岗位？

4. 技术颠覆风险：该领域是否面临被AI/新技术/新方法论替代的风险？（如：传统组学分析 vs AI驱动的组学，传统药物筛选 vs AI drug discovery）

5. 中国/国际差异：同一个领域在国内和国际的发展阶段可能不同（如：某领域在国内是政策热点但国际已趋于饱和，或反之）

信息来源：

• 领域顶刊的发表量年度趋势（PubMed/Scopus统计）
• 国家基金资助项目数量趋势（NSFC/NIH Reporter）
• 行业报告和市场分析（招聘网站、行业白皮书）
• 领域顶级会议的参会规模变化
• 知名课题组的方向转移信号

输出格式： 给出明确的趋势判断标签（萌芽/上升/成熟/衰退/夕阳）+ 置信度 + 关键证据 + 对学生的具体影响。

Phase 7: Multi-Dimensional Scoring

Read references/advisor_evaluation_framework.md for detailed rubrics.

Chinese context — 11 dimensions:

#	Dimension	Weight
1	Field Macro Trend (领域宏观趋势)	10%
2	Publication Output & Quality (发表成果与质量)	12%
3	Student Cultivation Track Record (学生培养实绩)	13%
4	Platform & Resources (平台与资源)	12%
5	Independence & Growth Space (独立性与成长空间)	8%
6	Career Trajectory & Momentum (职业轨迹与势头)	5%
7	PUA/Exploitation Risk (PUA/PUSH风险)	10%
8	Time Freedom (时间自由度)	8%
9	Goal-Advisor Match (毕业目标匹配)	7%
10	Advisor Sharp Critique (导师锐评)	10%
11	Retirement & Stability Risk (退休与稳定性风险)	5%

International context — 11 dimensions:

#	Dimension	Weight
1	Field Macro Trend	10%
2	Publication Output & Quality	12%
3	Student Outcome Track Record	13%
4	Institution & Lab Resources	12%
5	Mentorship & Independence Balance	8%
6	Career Trajectory & Momentum	5%
7	Toxicity / Exploitation Risk	10%
8	Work-Life Balance & Flexibility	8%
9	Goal-Advisor Match	7%
10	Advisor Sharp Critique	10%
11	Retirement & Stability Risk	5%

New dimensions explained:

• Field Macro Trend (D1): Replaces old "Research Direction & Prospects" with a much deeper, structured macro trend analysis (see Phase 6.5). Not just "is it a hotspot" but WHERE in the lifecycle, WHAT the job market looks like, and WHETHER the field faces disruption.
• Advisor Sharp Critique (D10): A synthesized, honest assessment that cuts through diplomatic scoring. See Phase 9.5 for details.
• Retirement & Stability Risk (D11): Evaluates whether the advisor will still be active and funded for the full duration of the student's degree.

Key difference: The Chinese "时间自由度" dimension evaluates freedom for 考公/考编/实习, which is irrelevant for international students. The international "Work-Life Balance" evaluates vacation policy, expected work hours, remote flexibility, and support for career development activities (conferences, internships, courses).

Phase 8: Red / Green Flag Check

Run through the flag checklists in references/advisor_evaluation_framework.md. Region-specific flags:

Universal red flags:

• No traceable student outcomes
• Extended time-to-degree pattern
• Students leaving mid-program
• No papers in 2+ years
• Funding gaps > 3 years
• Multiple PUA/toxicity reports online
• Retracted papers

Chinese-specific red flags:

• 横向 projects with no student benefit
• Only 硕导 but recruiting PhD-track students
• Excessive graduation requirements beyond norms
• No internship permission despite students wanting industry careers

International-specific red flags:

• High postdoc churn rate
• Lab members rarely listed as first/corresponding author
• No conference travel support
• Visa sponsorship issues for international students
• Advisor takes credit for student work (scooping)
• "Revolving door" lab (many short-tenure members)
• Glassdoor/Reddit reports of toxic culture

Universal green flags:

• Multiple student first-author papers in good journals
• Clear, positive student outcomes
• Recent promotion or awards
• Conference support for students
• Reasonable stipends
• Positive online reviews from current/former students

Phase 9: Advisor Sharp Critique (导师锐评)

不要让外交辞令害了学生。学生需要的不是3.8分还是4.1分的区别，而是"这个人到底能不能选"的直觉判断。

这个阶段是整个评估的灵魂。在完成所有数据收集和机械化打分后，用以下框架对导师进行一次不留情面的直觉评估。

锐评必须回答的7个问题：

1. 一句话判决：如果你的亲弟弟/亲妹妹问你能不能选这个导师，你会说什么？（不是写给学术委员会的，是写给家人的）

2. 导师的"人设"vs现实：

   • 导师对外展示的形象是什么？（官网简介、招生宣传、公开讲话）
   • 数据和学生评价反映的现实是什么？
   • 两者之间有多大差距？差距越大越危险。

3. 最大的隐藏风险：导师不会主动告诉你、但你入组后一定会遇到的问题是什么？（基于学生评价、出组率、发表模式推断）

4. 最被低估的优点：导师身上被分数系统低估的、真正有价值的特质是什么？

5. 5年后预测：根据导师的年龄、职称、资金、发表趋势、领域走向——5年后这个实验室会是什么状态？上升、稳定、还是衰退？

6. 替代方案建议：如果不选这个导师，在同一领域/同一学校，还有什么替代选择值得考虑？（基于合作者网络和同院系信息推断）

7. Deal-Breaker检查：是否存在以下任何一个"一票否决"条件？

   • 多条独立的PUA/toxicity投诉（不是一条可能是个人恩怨，多条就是系统性问题）
   • 导师3年内即将退休但没有明确的接班安排
   • 近3年完全无经费且无新论文
   • 多名学生中途退组/延期毕业的明确证据
   • 如果触发任何一条，无论其他维度分数多高，总评必须标注为"⚠️ 存在一票否决风险"

锐评的评分标准：

Score	Criteria
5	强烈推荐：数据和直觉都指向这是一个优秀的选择，几乎没有隐藏风险
4	推荐：整体良好，有小瑕疵但不影响大局，适合大多数学生
3	中性：有明显的优点也有明显的缺点，取决于学生个人情况和风险偏好
2	谨慎：存在显著风险信号，只推荐给特定类型的学生（如：极度自驱、不需要指导的）
1	不推荐：多个红灯信号，或存在一票否决条件

锐评的写作风格：

• 说人话，不说学术套话
• 用具体事实支撑判断，不空谈
• 敢于给出明确的"推荐/不推荐"结论，不骑墙
• 如果信息不足无法判断，直说"信息不足，无法给出可靠的锐评"，不要硬编

Phase 9.5: Retirement & Stability Risk Assessment

评估导师在学生就读期间是否会保持稳定。

检查项：

• 导师年龄/出生年份（推算退休时间）
• 是否临近退休年龄（中国：男60/女55，有延聘可能到65；国际：通常无强制退休但65+需关注）
• Tenure status（国际）：pre-tenure PI有被deny tenure导致实验室关闭的风险
• 经费连续性：当前经费何时到期？是否有续期迹象？
• 是否有实验室搬迁/跳槽迹象？（关注近期的职位变动、多个affiliation）
• 健康/精力信号：近年会议出席、论文产出是否有下降趋势

Score	Criteria
5	导师40-55岁，tenure/正教授，经费充足，至少10年稳定期
4	导师较年轻或中年，经费稳定，无退休/搬迁风险
3	有轻微风险信号（经费即将到期、pre-tenure），但总体可控
2	明显风险：导师55+岁无明确接班人，或pre-tenure且发表不够
1	高风险：导师即将退休、经费中断、或有跳槽/关闭实验室迹象

Phase 10: Report Generation

Output all investigation data as structured JSON, then render via scripts/generate_report.py:

python scripts/generate_report.py report_data.json -o "教授名_机构.html"

The JSON schema and 18-section report structure are defined in references/report_template.md. Key rules: output language matches input, every claim cites a source, 锐评 must be in the top 3 sections.

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Parallel Search Strategy

Launch searches in parallel batches to maximize efficiency:

• Batch 1: Faculty page + Scholar profile + Lab website + Thesis DB
• Batch 2: PubMed broad (NO keywords) + Scopus/OpenAlex + Name variants + Preprints
• Batch 3: Social platforms + News + Funding DBs + Retraction Watch
• Batch 4: Cross-validate counts + Verify student outcomes + Fill gaps

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Quality Rules

1. Every claim needs a source. No unsourced assertions.
2. Distinguish fact from inference. Mark speculative conclusions explicitly.
3. Cross-validate metrics. Use ≥3 databases for publication counts.
4. Weight recent evidence. Last 5 years matter more than career totals.
5. No fabrication. If information is unavailable, say so — don't guess.
6. Be balanced. Report both strengths and weaknesses.
7. Score vs peers. Compare against others at the same institution and rank.
8. Student signals > publication metrics. Always.
9. PUA/toxicity evidence is critical. Don't downplay concerning signals.
10. Publication gap verification. Complete the 6-step checklist before concluding any gap.

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Comparative Mode

When comparing multiple advisors: investigate each independently, generate individual reports, then add a comparison card with side-by-side scores, composite comparison, and trade-off analysis.

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Integration with Other Skills

Leverage these skills when available:

• pubmed-database, openalex-database — Publication searches
• deep-research, exa-search — Web research and social platform mining
• biorxiv-database, arxiv-database — Preprint searches
• scientific-visualization, matplotlib — Charts in report
• literature-review — Systematic publication analysis
• citation-management — Reference verification
• scripts/robust_fetch.py — Anti-bot web fetch with 3-layer fallback (derived from Web-Rooter, MIT)
• scripts/search_social.py — Chinese social platform search (知乎/小红书/小木虫/贴吧/保研论坛/考研帮)

Chinese Website Fallback (zero dependencies, details in references/web_rooter_integration.md)

python scripts/robust_fetch.py "<URL>"                                     # auto fallback
python scripts/robust_fetch.py "<URL>" --js                                # force browser
python scripts/search_social.py "导师名 大学名" --platforms zhihu,xiaohongshu,emuch  # social search

• URL freshness: if 404, re-search via WebSearch("site:<domain> 教授姓名")
• If wr available: prefer wr html/wr social (see references/web_rooter_integration.md)