# Interview Prep > >- - **Type:** Skill - **Install:** `agentstack add skill-bydeng01-phd-application-skill-interview-prep` - **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/interview-prep ## Install ```sh agentstack add skill-bydeng01-phd-application-skill-interview-prep ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Interview prep A PhD interview is a two-way fit conversation: the professor is checking whether the applicant thinks well, knows their own work, and would be good to mentor; the applicant is checking whether the lab is right for them. Good preparation isn't memorizing answers — it's anticipating the real questions this specific professor would ask and having thought through genuine, specific responses. The aim is for the applicant to walk in able to speak confidently about their work, the lab's work, and the connection between them. ## Step 1 — Load the context Read the professor's profile at `knowledge-base/professors/.md` (agenda, recent papers, open problems), the application materials at `knowledge-base/applications//` (the SOP and proposal — they'll be asked about), and the applicant's `profile/` (background and the work they'll be expected to discuss). The more the questions are tied to *this* professor's actual research, the more useful the prep. If no professor profile exists, run **professor-analyzer** first — generic interview questions are far less useful than ones grounded in the specific lab. ## Step 2 — Generate questions across the real categories Cover the categories a PhD interview actually spans. For each question, write a *model answer* drawn from the applicant's real material plus a short coaching note on what the interviewer is really probing and how to handle it well. - **Motivation & fit** — why a PhD, why this lab, why now. (Probing: genuine, specific interest vs. scattershot applying.) - **The applicant's own work** — deep questions about their MS thesis / projects / papers: why this approach, what they'd do differently, what they learned. (Probing: do they own their work and think critically about it.) - **The proposed research / the lab's work** — questions about their proposal and the professor's recent papers. (Probing: can they engage with the lab's actual problems.) - **Technical depth** — field-appropriate fundamentals the professor would expect. (Probing: foundations and how they reason through a problem.) - **Behavioral / working style** — collaboration, handling failure, independence. - **Questions the applicant should ask back** — thoughtful questions about the lab (funding, mentorship style, current projects, where students go after). Asking good questions is itself evaluated, and helps the applicant choose well. Prioritize questions this specific professor is *likely* to ask given their work, not a generic bank. Where a strong answer needs a specific only the applicant has, mark it with `[brackets]` and coach them on how to fill it. ## Step 3 — Save and offer a mock Write the prep to `knowledge-base/applications//interview.md`: questions grouped by category, each with a model answer and a coaching note, plus the applicant's questions-to-ask and a short list of likely-discussed papers to review beforehand. Then offer an **interactive mock interview**: you play the professor, ask questions one at a time, let the applicant answer, and give specific feedback. This rehearsal is where prep turns into confidence — many applicants want it once they see the question list. ## Guardrails Per `shared/references/ethics.md`, model answers must be grounded in the applicant's real experience — coach them to articulate what's true, not to memorize impressive-sounding fabrications they can't back up under follow-up questioning (interviews are designed to expose exactly that). Keep the tone encouraging and constructive; the goal is a prepared, confident applicant who can have a genuine conversation, not a scripted one. ## 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-interview-prep - 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%.