Multi-platform skill. This
SKILL.mdis 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-rootAGENTS.md. Cursor reads.cursor/rules/deeptutor.mdc. All three documents describe the same workflow; updates should be propagated toAGENTS.mdwhen changing the workflow itself.
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.
The report renderer currently supports Chinese and English. Chinese input produces a fully Chinese report; English input produces a fully English report. If the user requests another language, ask whether an English report is acceptable before starting. Never mix languages within a report except when quoting original sources with a translation.
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.
Minimum input: Professor name + institution name.
If the user hasn’t provided these, ask:
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.
DeepTutor has two investigation modes. The right mode depends on the model running it.
Full Version (完整版) — run without asking:
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.
If the user chooses Lite, read references/lite_mode.md for the full specification. Key differences:
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.
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:
Chinese-specific additions:
International-specific additions:
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.
This phase implements the “ceiling principle.” Track as many current and former students as possible.
How to find students:
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:
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:
Analyze:
Build a co-author frequency table from the publication record. Classify relationships:
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.
Chinese institutions → Read references/chinese_academic_system.md:
International institutions → Read references/international_academic_system.md:
Assess:
This phase uses region-specific platforms to gather student reviews and lab culture signals.
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 | muchong.com | Grad student discussions, lab reputation |
| 保研论坛 | eeban.com | 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.
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).
Combine BOTH Chinese and international platforms, plus:
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：
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？
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）
信息来源：
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。
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:
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).
Run through the flag checklists in references/advisor_evaluation_framework.md. Region-specific flags:
Universal red flags:
Chinese-specific red flags:
International-specific red flags:
Universal green flags:
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）
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：根据官方任期/招生安排、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）
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：
评估导师在学生就读期间是否会保持稳定。
检查项：
| Score | Criteria |
|---|---|
| 5 | 官方任期/招生计划与经费覆盖学位周期，tenure/稳定职位，无搬迁或停招信号 |
| 4 | 经费和职位稳定，现有证据未见会影响学位周期的风险 |
| 3 | 有轻微风险信号（经费即将到期、pre-tenure），但存在可核验的缓解安排 |
| 2 | 明显风险：任期/经费可能在学位周期内结束，且接班或共同指导安排不明确 |
| 1 | 高风险：已公告即将退休/离职/停招、经费中断，或实验室有关闭迹象且无安置方案 |
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: use the selected Chinese or English language consistently, cite every claim, and keep 锐评 in the top 3 sections.
Launch searches in parallel batches to maximize efficiency:
search_social.py sends query terms to Bing and may fall back to Sogou, so never include private or sensitive information in a query.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.
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 — Public-network-only stdlib fetch with DNS/IP pinning; optional curl/browser interfaces fail closed (adapted from Web-Rooter, MIT; see THIRD_PARTY_NOTICES.md)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)references/web_rooter_integration.md)python scripts/robust_fetch.py "<URL>" # pinned stdlib HTTP(S) only
# --js is intentionally disabled because browser networking cannot preserve the pinned-IP boundary
python scripts/search_social.py "导师名 大学名" --platforms zhihu,xiaohongshu,emuch # social search
WebSearch("site:<domain> 教授姓名")wr available: prefer wr html/wr social (see references/web_rooter_integration.md)