jiadizhunine/deeptutor

DeepTutor — Academic Advisor Investigation System

Multi-platform skill. This SKILL.md is the entry for Claude Code / agentskills.io. For Codex CLI, OpenCode, OpenClaw, Aider, Cline, Continue, and any other tool following the agents.md spec, the equivalent entry point is the repo-root AGENTS.md. Cursor reads .cursor/rules/deeptutor.mdc. All three documents describe the same workflow; updates should be propagated to AGENTS.md when changing the workflow itself.

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+
• GPT-5 / GPT-5-Codex (powering Codex CLI) and equivalent
• Gemini 2.5 Pro / Gemini 3 and equivalent
• Future flagship models of equivalent or higher capability

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

⚠️ DeepTutor 模式选择 检测到当前模型非旗舰级别。

• 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？

If unsure of the running model's class, default to prompting rather than silently running Full — a Lite report from a weaker model beats a hallucinated Full report.

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)