Professor Fit Analyzer

Professor Fit Analyzer

Build an evidence-based dossier for a professor and convert that dossier into an application, advising, or collaboration strategy.

This skill is platform-agnostic, discipline-agnostic, and level-agnostic. It serves PhD applicants, master's students seeking thesis advisors, and undergraduate students looking for capstone/thesis/independent-study supervisors — across all academic fields. Use the browsing, search, and file-reading tools available in the current agent. Prefer public academic sources first. If browsing is unavailable, ask the user for links, PDFs, or a publication list and continue with partial coverage rather than guessing.

Input Specification

Required inputs

• Professor name.
• At least one of: Google Scholar URL, personal website URL, lab website URL.

Optional but recommended inputs

• User education background (current degree, field, institution).
• User research experience (past projects, publications, technical skills).
• Application type: PhD student, master's student (thesis-track or coursework-track), undergraduate researcher (capstone/thesis/independent study/專題), RA, postdoc, visiting scholar, or research collaborator.
• User strengths and distinctive capabilities.
• Nationality, language proficiency, and whether scholarship or visa sponsorship is needed.
• Target timeline (e.g., Fall 2027 PhD application, this summer for internship, immediate contact).

Input handling rules

• If the user provides only a professor name with no URL, ask for at least one URL or publication source before proceeding.
• If user background is missing, produce conditional advice by applicant type and level rather than generic praise.
• If target timeline is missing, assume general exploration and skip urgency-dependent advice.
• If application type is missing, ask the user to specify their level (PhD / master's / undergraduate / other). The analysis depth and advice framing differ significantly by level.
• If the user provides a CV or publication list, use it to strengthen fit analysis.

Language Policy

Default output language is Traditional Chinese (繁體中文).

Exceptions — keep in English:

• paper titles,
• venue names (e.g., NeurIPS, ACL, Nature, The Lancet, American Economic Review),
• method and model names (e.g., Transformer, CRISPR, difference-in-differences),
• technical terms without widely accepted Chinese translations,
• proper nouns (person names, institution names).

If the user writes in English, switch all output to English. If the user explicitly requests a different language, follow that request.

Operating Principles

1. Separate facts, inferences, and speculation.
2. Prefer repeated signals over one-off anecdotes.
3. Do not equate citation count, h-index, or raw publication count with quality.
4. Do not equate high output with good mentoring or overwork.
5. Do not equate clear writing with guaranteed teaching quality.
6. Treat authorship interpretation cautiously. State uncertainty. Authorship norms vary by field (see Field Profile below).
7. Use the applicant's goals, risk tolerance, level, and background as the center of final advice.
8. Calibrate analysis depth and advice to the applicant's level (see Applicant-Level Calibration below).
9. Never claim to have read a paper that you could not access.

Workflow Overview

Follow this sequence:

1. Detect the academic field and establish field-specific conventions.
2. Build source coverage.
3. Construct the publication timeline.
4. Cluster papers into a research taxonomy.
5. Read papers and extract structured evidence.
6. Synthesize professor-level judgments.
7. Infer future directions and possible openings.
8. Produce applicant-fit analysis and professor-student type matching.

7B. Check for warning signs.

1. Draft tailored outreach emails.
2. Produce a conversation and interview playbook.
3. End with actionable next steps.

If the professor has a very large corpus, do not silently downsample. Use the coverage policy below.

Applicant-Level Calibration

The analysis depth, fit analysis, outreach strategy, and advice framing should adapt to the applicant's level and field.

PhD applicant

• Full-depth analysis is appropriate.
• Evaluate long-term research trajectory fit (3-5+ years).
• Emphasize mentoring quality, student outcomes, publication opportunity, and advisor-student working style.
• Outreach should demonstrate research maturity and concrete contribution.
• Warning signs about student development and lab culture deserve full scrutiny.
• Humanities/social sciences: PhD duration is often 5-7+ years. Advisor fit is about intellectual compatibility, theoretical alignment, and mentoring on writing and argumentation — not just "research output." Publication expectations during the PhD vary widely.
• Biomedical sciences: PhD involves extensive lab rotations and bench work in many programs. Evaluate whether the professor has wet-lab or clinical resources, and whether students get first-author papers on substantial projects (not just middle-author credits on large-team papers).

Master's student (thesis-track)

• Focus on whether the professor has experience advising master's theses.
• Evaluate 1-2 year fit rather than long-term trajectory.
• Emphasize: topic alignment, advisor responsiveness, graduation timeline, and whether the professor's current projects have master's-accessible entry points.
• Outreach can be more straightforward — show interest, show preparation, show willingness to learn.
• Look for whether the professor has had master's students before and what they worked on.
• Humanities/social sciences: A master's thesis may involve archival research, textual analysis, ethnographic fieldwork, or a philosophical argument. Look for whether the professor supervises theses in these methodologies and whether the topic scope is achievable in 1-2 years.
• Biomedical sciences: A master's thesis may involve a defined experimental project, a clinical data analysis, or a literature-based systematic review. Look for well-bounded experimental questions that don't require multi-year data collection.

Master's student (coursework-track seeking a project)

• Even lighter-touch analysis.
• Focus on whether the professor has short-term projects or well-scoped problems.
• Emphasize: how quickly the student can become productive, and whether the project scope fits a semester or two.

Undergraduate researcher (capstone/thesis/independent study/專題)

• The lightest-touch variant, but still evidence-based.
• Focus on whether the professor is known to take undergraduates, whether the research has accessible entry points, and whether the professor has a track record of mentoring at this level.
• Look for: undergraduate co-authors in past publications, mentions of undergraduate mentoring on lab/personal pages, well-scoped sub-problems.
• Outreach should be humble but specific — show knowledge of the research area, show enthusiasm, and propose a concrete skill or background the student brings.
• The fit analysis should address: learning opportunity, skill development, and whether the experience will strengthen graduate school applications or career preparation.
• Warning signs focus on: professor being too busy to mentor undergraduates, research requiring prerequisites the student lacks, or no history of undergraduate involvement.
• Humanities/social sciences: Undergraduate research might involve literature reviews, archival work, interview transcription and analysis, translation, or editorial assistance. The student may bring language skills, close-reading ability, cultural knowledge, or familiarity with specific historical periods or regions.
• Biomedical sciences: Undergraduate research often starts with assisting in ongoing experiments, learning protocols, data entry, or literature searches. The student may bring lab course experience, basic statistics, programming for data analysis, or volunteer/clinical shadowing experience.

RA / Postdoc / Visiting Scholar / Collaborator

• Full-depth analysis similar to PhD level.
• Adjust fit analysis to the specific role.

Step 0. Detect Academic Field and Establish Field Profile

Before proceeding, determine the professor's primary academic field from their department, publication venues, and research keywords. Then establish a field profile that governs how subsequent steps are interpreted.

Field profile dimensions

Determine each of the following for the professor's field. If uncertain, state the uncertainty and use the most conservative interpretation.

Publication culture:

• conference-driven (CS, ML, HCI): primary outputs are peer-reviewed conference papers. Rapid cycle (6–12 months). Conference acceptance is the prestige signal.
• journal-driven (biology, medicine, physics, chemistry, economics, most social sciences): primary outputs are journal articles. Cycle 1–3 years including review. Impact factor and journal tier matter.
• monograph-driven (humanities, law, some social sciences): books and book chapters are major outputs. A single monograph may represent years of work.
• mixed: some fields (e.g., information science, education, HCI) use both conferences and journals as primary venues.

Authorship norms:

• first-and-last (CS, biology, biomedicine): first author did most work, last author is the PI. Middle authors contributed but did not lead.
• alphabetical (mathematics, theoretical economics, theoretical physics): author order does not imply contribution. Contribution is inferred from acknowledgments or field knowledge.
• single-author common (humanities, philosophy, mathematics): solo authorship is normal and prestigious. Few co-authored papers is not a red flag.
• large-team (high-energy physics, genomics, clinical trials): papers may have dozens to hundreds of authors. Individual contribution is hard to assess from author lists alone.

Typical lab structure:

• large research group (experimental sciences, ML labs): 10–30+ members including PhD students, postdocs, technicians, RAs.
• small research group (theory, humanities, some social sciences): 1–5 students. Close mentoring. Low output volume is normal.
• no lab (solo scholars in humanities, pure math): professor may work alone or with 1–2 students. Judging by "lab size" is inappropriate.

Output volume norms:

• high volume (ML, biomedicine): 10–40+ papers/year for a productive group is normal.
• moderate volume (most experimental sciences, social sciences): 3–10 papers/year.
• low volume (humanities, pure math, theoretical physics): 1–3 papers/year. A "slow" output rate may indicate depth, not weakness.

Resource intensity:

• compute-heavy (ML, computational sciences): GPU clusters, cloud compute.
• equipment-heavy (experimental physics, chemistry, biology): specialized instruments, clean rooms, wet labs.
• data-heavy (genomics, epidemiology, economics): large datasets, clinical cohorts, survey infrastructure.
• clinical-access-heavy (clinical medicine, public health, nursing research): hospital IRB access, patient cohorts, clinical trial infrastructure, medical records databases.
• fieldwork-heavy (anthropology, ecology, archaeology): field access, travel, long data collection periods.
• human-subjects-heavy (psychology, education, sociology, public health): IRB/ethics approval, participant recruitment, interview infrastructure, longitudinal cohort maintenance.
• archival/interpretive (humanities, history, law): library access, archival collections, language skills, rare manuscript access, museum or gallery collaborations.
• creative-practice (arts, design, music, creative writing, film): studio access, exhibition opportunities, performance venues, creative portfolios as scholarly output.
• minimal infrastructure (pure math, theoretical CS, philosophy): primarily intellectual resources.

Mentoring culture:

• structured lab meetings (experimental sciences, ML): regular group meetings, journal clubs, progress reports.
• seminar and reading group (humanities, social sciences, theoretical fields): intellectual development through discussion, close reading, and critique.
• clinical mentoring (medicine, nursing, public health): combines research mentoring with clinical training; students may have dual roles as clinicians and researchers.
• studio critique (arts, architecture, design): feedback through portfolio review, exhibition preparation, and iterative creative process.

How the field profile affects analysis

• Publication timeline (Step 2): In conference-driven fields, look for bursts near conference deadlines. In journal-driven fields, look for clusters around grant reporting periods or thesis milestones. In monograph-driven fields, gaps of 2–3 years between publications are normal. In clinical research, publication rhythm may follow clinical trial phases or cohort availability.
• Novelty categories (Step 4): Use field-appropriate categories (see Step 4 below).
• Resource profile (Step 5): Assess resources that matter for the field, not a generic compute checklist. For biomedical researchers, emphasize clinical access, IRB infrastructure, and funding sources (NIH, MOST, NHS, etc.). For humanities scholars, emphasize archival access, language capabilities, and institutional affiliations that enable specialized research.
• Workload assessment (Step 5): Calibrate "high output" against field norms, not a universal threshold.
• Authorship analysis (Step 4): Interpret author order according to field convention. In biomedicine, corresponding author (often last) is the PI; in humanities, solo authorship is the norm.
• Warning signs (Step 7B): Calibrate "paper slicing" against field norms for publication granularity. In clinical medicine, separate papers per cohort or trial phase may be standard. In humanities, a single definitive article or monograph chapter may represent years of work.
• Student skill expectations (Step 7): In biomedical fields, students need wet-lab or clinical skills, statistics, and often coding for bioinformatics. In humanities, students need close-reading ability, archival competence, theoretical sophistication, and often language proficiency. Do not evaluate students from one field by the skill expectations of another.

Coverage Policy

Default target

Aim to cover the full publication list that is realistically accessible.

When the corpus is large

If the professor has too many papers to fully inspect in one pass, use a transparent tiered policy:

• Tier 1: Core papers. Recent papers, highly central papers, representative papers from each research line, and papers that appear to define transitions.
• Tier 2: Supporting papers. Additional papers needed to validate taxonomy, collaboration patterns, and student trajectories.
• Tier 3: Peripheral papers. Minor, redundant, workshop, demo, or inaccessible papers. Summarize these more lightly.

Always report:

• how many total papers were identified,
• how many were read in depth,
• how many were skimmed or inferred from metadata,
• which conclusions depend on partial coverage.

Source Collection Protocol

Prioritize these sources in roughly this order, selecting the field-appropriate ones:

1. Google Scholar profile or publication list.
2. Personal website and lab website.
3. Field-specific bibliographic databases:

• CS/ML: DBLP, ACL Anthology
• Biomedicine/clinical medicine: PubMed, Europe PMC, ClinicalTrials.gov
• Public health/epidemiology: PubMed, Global Health (CABI)
• Nursing/allied health: CINAHL, PubMed
• Psychology: PsycINFO, PubMed
• Social sciences/economics: SSRN, RePEc, EconLit, ProQuest Social Sciences
• Education: ERIC, EdArXiv
• Humanities/philosophy: PhilPapers, JSTOR, Project MUSE
• Literature/languages: MLA International Bibliography, JSTOR
• History: Historical Abstracts, JSTOR, digitized archives
• Law: Westlaw, HeinOnl

…

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: voidful
• Source: voidful/academic-skills
• License: MIT

Install and usage instructions live in the source repository linked above.