# Preview Interview

> Prepare for upcoming interviews by previewing likely questions, structuring strong answers, and running mock sessions. Use when Codex needs to help with interview preparation, recruiter screens, technical or product interviews, behavioral stories, resume-to-role gap analysis, question banks, answer critique, or pre-interview rehearsal.

- Skill: `nordz0r/preview-interview` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add nordz0r/preview-interview`
- Raw SKILL.md: https://api.skillmd.com/api/skills/nordz0r/preview-interview/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: nordz0r (https://skillmd.com/u/nordz0r)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/nordz0r/preview-interview

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# Preview Interview

Use this skill to turn a role, resume, or job description into a focused
interview-preparation package. Start from the target role and interviewer style,
then produce only the practice materials that help the user perform better.

## Core Workflow

1. Identify the interview target.
- Extract the role, seniority, company context, and interview type.
- Infer whether the user needs recruiter screening, behavioral practice,
  technical depth, system design, product sense, or management questions.

2. Build the interview map.
- List the skills and signals the interviewer will likely test.
- Translate the job description and resume into likely question themes.
- Separate must-cover topics from nice-to-have topics.

3. Generate the prep set.
- Draft likely questions with increasing difficulty.
- Build short answer outlines before long polished answers.
- Use concrete evidence, metrics, tradeoffs, and decision points.

4. Rehearse and critique.
- Run a mock interview in the same tone as the expected round.
- Score answers on clarity, relevance, specificity, and brevity.
- Rewrite weak answers with tighter structure and stronger examples.

5. Close the gaps.
- Identify missing stories, thin technical depth, or unsupported claims.
- Recommend what to study, what to cut, and what to emphasize.

## Deliverables

Return only the pieces that fit the request:

- A likely question bank by interview round.
- STAR or CAR answer skeletons for behavioral questions.
- A mock interview script with follow-up prompts.
- A concise critique of draft answers.
- A gap analysis between resume and job description.
- A short pre-interview checklist for the final review.

## Decision Rules

- Prefer role-specific questions over generic interview lists.
- Prefer concise answer outlines before polished final wording.
- Push for evidence: metrics, scope, constraints, and outcomes.
- Flag unsupported resume claims and missing examples early.
- If the role is unclear, infer the most likely interview format from the job
  description and state the assumption.

## Common Failure Modes

- Answers stay generic and do not show ownership.
- Stories omit numbers, scale, tradeoffs, or user impact.
- Technical answers jump to tools before clarifying requirements.
- Behavioral answers are too long and never reach the result.
- The prep set does not match the likely interview round.

## References

Load [interview-patterns.md](references/interview-patterns.md) for reusable
frameworks, answer structures, and mock-interview templates.

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## Routing signals: interview prep mock interview behavioral technical recruiter screening answer structure star resume role seniority questions practice feedback
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