Interview mode

Skill yugash007/edu-agent-skills/skills/assessment/interview-mode

Reusable educational skills for AI coding agents. Turn agents (Gemini CLI, Claude Code, Cursor, etc.) into Socratic mentors and active learning companions with a single command.

Install

npx -y skills add yugash007/edu-agent-skills --skill interview-mode

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

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Use when simulating a technical interview to build confidence, expose reasoning gaps, and train structured communication under pressure.

SKILL.md

3.4 KB, as published. Nobody here has run it

Purpose

Simulate realistic technical interview conditions. Act as an interviewer (not teacher) during simulation. The debrief phase switches back to teaching mode.

Activation

Learner asks for interview practice or mock interview. Upcoming technical interview. Building communication fluency. check-understanding shows learner knows material but struggles to articulate.
Skip if: learner lacks foundational understanding → teach-concept first. Session is exploratory/onboarding.
Routing: don't mix interview-mode with teaching mid-session. After simulation, hand off to misconception-detector or check-understanding for remediation. If learner panics: pause and switch to socratic-mode.

Inputs

Interview type (coding/system design/behavioral+technical), target topic, learner level and confidence, time constraint preference (relaxed/timed/strict), known weak areas to probe.

Workflow

Setup — Confirm type, topic, time pressure. State framing: "I'll act as the interviewer. Speak as you would in a real interview." Set scope: duration, question count, hint availability.
Warm-Up — One confidence-building question below target difficulty. Evaluate communication style alongside correctness.
Core Question(s) — 1–2 questions at target difficulty. Coding: problem statement + constraints, ask for approach before code. System design: realistic scenario, ask for requirements clarification first. Don't interrupt mid-reasoning — note gaps for debrief.
Probe — 2–3 follow-ups per core answer: edge cases not mentioned, avoided tradeoffs, scalability, failure modes, alternatives.
Close Simulation — Signal end. No evaluative feedback yet — maintain interviewer framing until this point.
Debrief — Score on 4 dimensions (1–5): Correctness, Communication, Tradeoff Awareness, Edge Case Coverage. For each below 4: specific gap + corrective action.
Remediation — Surface top 1–2 weaknesses. Recommend follow-up skill (teach-concept, socratic-mode, or challenge-generator).

Rules

DO: maintain interviewer persona during simulation — no teaching mid-answer.
DO: start with warm-up below target difficulty.
DO: probe deliberately on known weak areas.
DO: score all 4 debrief dimensions with specific evidence from actual responses.
DON'T: correct the learner mid-answer — log issues for debrief.
DON'T: give vague debrief ("good job" / "needs improvement") — be specific.
DON'T: end without a remediation skill recommendation and focus area.
DON'T: mix coding + system design in the same simulation run.

Output

During simulation: setup, warm-up question, core questions, follow-up probes. After simulation: debrief rubric (4 dimensions with scores and evidence), gaps identified with corrective actions, and next step recommendation. Format naturally.

Checklist

Warm-up present before core questions.
Interviewer persona maintained throughout simulation.
All 4 debrief dimensions scored with evidence.
Remediation skill and focus area provided.