Skill snapshot iter1

Skill bydeng01/phd-application-skill/skills/professor-analyzer-workspace/skill-snapshot-iter1

A field-agnostic AI skill suite that automates PhD application research, outreach, and tracking via a shared, version-controlled knowledge base.

Install

npx -y skills add bydeng01/phd-application-skill --skill skill-snapshot-iter1

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Deep-dive a professor or research lab to assess PhD fit. Use this whenever the user wants to research, analyze, profile, or "look into" a potential PhD supervisor, advisor, faculty member, or lab — including questions like "is Prof X a good fit for me?", "what is this lab working on?", "analyze Professor Smith's recent work", "should I email this professor?", or when they paste a faculty page, Google Scholar link, or lab URL and want it understood. Gathers recent publications, grants, lab agenda, and open problems, then scores fit against the applicant's profile and surfaces concrete outreach hooks. Writes a structured profile to knowledge-base/professors/. Trigger even if the user doesn't say the word "analyze" — any intent to evaluate a specific researcher for PhD applications should use this skill.

SKILL.md

4.9 KB, as published. Nobody here has run it

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.