# Professor Analyzer > >- - **Type:** Skill - **Install:** `agentstack add skill-bydeng01-phd-application-skill-skill-snapshot-iter1` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [bydeng01](https://agentstack.voostack.com/s/bydeng01) - **Installs:** 0 - **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [bydeng01](https://github.com/bydeng01) - **Source:** https://github.com/bydeng01/phd-application-skill/tree/main/skills/professor-analyzer-workspace/skill-snapshot-iter1 ## Install ```sh agentstack add skill-bydeng01-phd-application-skill-skill-snapshot-iter1 ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # 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/.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/.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. ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [bydeng01](https://github.com/bydeng01) - **Source:** [bydeng01/phd-application-skill](https://github.com/bydeng01/phd-application-skill) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-bydeng01-phd-application-skill-skill-snapshot-iter1 - Seller: https://agentstack.voostack.com/s/bydeng01 - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.