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Preview Interview

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Prepare for upcoming interviews: FAANG, STAR answers, mock sessions, recruiter screens, behavioral stories, question banks, answer critique, pre-interview rehearsal. Use for интервью, финальный interview, rehearsal — not visual UI design systems or admin-panel redesign.

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  • Added October 14, 2026
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Scanned October 14, 2026

npx -y skills add nordz0r/skills --skill preview-interview --agent claude-code

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SKILL.md
---
name: preview-interview
description: "Prepare for upcoming interviews: FAANG, STAR answers, mock sessions, recruiter screens, behavioral stories, question banks, answer critique, pre-interview rehearsal. Use for интервью, финальный interview, rehearsal — not visual UI design systems or admin-panel redesign."
---

# 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.

<!-- A-EVOLVE-ROUTING-SIGNALS:START -->
## Routing signals: interview prep mock interview behavioral technical recruiter screening answer structure star resume role seniority questions practice feedback
<!-- A-EVOLVE-ROUTING-SIGNALS:END -->

Files in this skill

  • SKILL.md3.1 KB
  • agents/openai.yaml259 B
  • references/interview-patterns.md2.2 KB

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