Professor / lab analyzer

This is the load-bearing skill of the PhD copilot: outreach emails, proposals, and ranking all consume the profile it produces. The goal is a profile so specific that a cold email built from it could only have been written to this professor — the opposite of generic.

What you produce

A single file at knowledge-base/professors/<slug>.md following the professor schema in shared/schemas/README.md (read it for the exact front-matter fields and section headings). Match that shape exactly so downstream skills can parse it.

Step 1 — Load context

Read knowledge-base/profile/profile.md (and profile/cv-master.md if fit detail is needed). You cannot assess "fit" without knowing the applicant's interests, background, and dealbreakers. If the profile is empty, ask the user for their research interests before proceeding — fit scoring is meaningless otherwise.

Identify the professor from the user's request: a name + institution, a homepage, a Scholar profile, or a lab URL.

Step 2 — Gather evidence

Use shared/references/data-sources.md for where to look. Prioritize recency — a PhD starts in 1–2 years, so what matters is where the lab is heading, not its decade-old greatest hits. Aim to ground every later claim in a real source.

Gather, roughly in priority order:

• Recent publications (last ~3 years). Titles, venues, and — crucially — the takeaway of each: what problem, what approach, what's new. Use publication search (arXiv, Semantic Scholar/OpenAlex, Scholar, PubMed as fits the field). Static pages → web fetch; rendered pages like Google Scholar → browser tools.
• Lab website / "join us" page. Current projects, stated open problems, whether they're recruiting, funding mentions.
• Grants / funding signals. Active grants suggest funded positions. Note explicitly when funding is unclear — don't guess.
• Research trajectory. Read across the recent papers for the direction: what thread is the lab pulling on, what will the next few papers likely be about?

If a source can't be fetched, note the gap rather than fabricating around it.

Step 3 — Assess fit

This is the part that makes the profile useful. Compare the professor's trajectory against the applicant's profile and be honest and specific:

• Overlaps — concrete intersections between their open problems and the applicant's interests/skills. Name the paper and the matching part of the applicant's background.
• Gaps — where the applicant lacks relevant background, or the lab's direction diverges from their goals. Real assessment includes the misses.
• fit_score (0–100) — your overall judgment, justified by the overlaps/gaps above, not a vibe. Reserve 80+ for strong, well-evidenced matches.
• Funding & "accepting students" signals, set honestly to unknown when unclear.

Step 4 — Outreach hooks

The payoff section. List 2–4 specific things the applicant could reference in a first email: a particular recent paper and a genuine, substantive reaction or question; a connection between one of their projects and the applicant's work; an open problem the applicant is positioned to contribute to. These must be real and specific — they are what separates an authentic email from spam. No flattery, no invented enthusiasm.

Step 5 — Write the file

Write knowledge-base/professors/<slug>.md with all schema fields and a ## Sources section linking everything you used. Then give the user a short summary: the fit verdict, the single strongest hook, and any gap they should be aware of. If you found a clear opening, suggest the natural next step (e.g. drafting outreach).

Guardrails

Follow shared/references/ethics.md. The cardinal rule: never invent a publication, finding, grant, or shared interest. If you're unsure whether something is real, mark it uncertain and cite what you actually found. A profile that honestly says "weak fit" is more valuable than a flattering one that wastes the applicant's outreach on a bad match.