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:
- 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
- Risk tolerance: Conservative / Moderate / Aggressive
- 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.
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.
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:
- Broad PubMed/Scopus/Scholar search with name + institution (NO topic keywords)
- Author ID-anchored search (Scopus ID, ORCID, Semantic Scholar)
- All name variants from the romanization template
- 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 |
|---|---|---|
| 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.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：
-
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
-
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？
-
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？
-
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）
-
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：
-
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）
-
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。
-
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）
-
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？
-
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）
-
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.
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
Quality Rules
- Every claim needs a source. No unsourced assertions.
- Distinguish fact from inference. Mark speculative conclusions explicitly.
- Cross-validate metrics. Use ≥3 databases for publication counts.
- Weight recent evidence. Last 5 years matter more than career totals.
- No fabrication. If information is unavailable, say so — don't guess.
- Be balanced. Report both strengths and weaknesses.
- Score vs peers. Compare against others at the same institution and rank.
- Student signals > publication metrics. Always.
- PUA/toxicity evidence is critical. Don't downplay concerning signals.
- Publication gap verification. Complete the 6-step checklist before concluding any gap.
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.
Integration with Other Skills
Leverage these skills when available:
pubmed-database,openalex-database— Publication searchesdeep-research,exa-search— Web research and social platform miningbiorxiv-database,arxiv-database— Preprint searchesscientific-visualization,matplotlib— Charts in reportliterature-review— Systematic publication analysiscitation-management— Reference verificationscripts/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
wravailable: preferwr html/wr social(seereferences/web_rooter_integration.md)