xujingchen1996/research-app-toolkit/interview-prep

Claude Code and agent skill by xujingchen1996/research-app-toolkit

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Checking that the SKILL.md file is still in the repository.

This skill should be used when the user asks to "prepare for interview", "practice interview questions", "mock interview", "interview preparation", or runs /ra:interview-prep. It prepares the user for research program interviews.

Install

npx vskill@latest install xujingchen1996/research-app-toolkit/interview-prep

SKILL.md

Interview Preparation

1. Prerequisite Check

Read ${CLAUDE_PLUGIN_ROOT}/.local.md. Check if cv_profile_analyzed is true and the ## CV Profile section is populated.

If cv_profile_analyzed is not true or the ## CV Profile section is empty, output:

未检测到 CV 画像。 请先运行 /ra:cv-analyze 分析你的 CV,其他功能依赖 CV 画像数据。

Output this message and stop immediately.

Note: This skill does NOT support personalization mode. It proceeds directly with the standard flow.

2. Identify Interview Target

If $ARGUMENTS is provided

Parse the professor name and university from the arguments. The expected format is:

[professor-name] [university]

Example: /ra:interview-prep John Smith MIT

If $ARGUMENTS is empty

Ask the user using AskUserQuestion:

  1. "你要准备哪个教授的面试?请提供教授姓名。"
  2. "这位教授在哪个大学?"

3. Research Process

3.1 Research the Professor
  1. Use WebSearch to find the professor's homepage and recent publications
  2. Search for: "[professor name] [university] recent papers [year]"
  3. Search for: "[professor name] [university] research group"
  4. Identify 3-5 key recent papers from the professor's group
3.2 Read Key Papers

For each key paper identified:

  1. Use mcp__web_reader__webReader to read the paper's abstract and key sections
  2. Extract: research questions, methodology, key findings, and limitations
  3. Note how this paper connects to the user's CV profile and research interests
3.3 Research the Program
  1. Use WebSearch to find the program's interview format and process
  2. Search for: "[university] [program] interview format"
  3. Search for: "[university] [program] interview experience" (for student reports)
  4. Determine: interview duration, format (technical/behavioral/both), panel composition

4. Generate Question Categories

Based on the research, generate mock interview questions organized into the following categories:

4.1 Research Background Questions

Questions testing knowledge of the professor's work:

  • "Can you explain Professor [name]'s work on [topic]?"
  • "What do you think about [method/approach] used in their recent paper?"
  • "How does Professor [name]'s research connect to broader trends in [field]?"
  • "What limitations do you see in [specific paper/approach]?"
4.2 Technical Skills Questions

Based on the user's cv_profile technical skills, generate questions that test depth of knowledge:

  • For each major technical skill in the user's CV, prepare 1-2 probing questions
  • Focus on skills most relevant to the professor's research area
  • Include questions about methodology choices and trade-offs
4.3 Motivation & Goals Questions
  • "Why did you choose this program?"
  • "What specific research questions interest you?"
  • "How does your background prepare you for research in [field]?"
  • "Where do you see yourself in 5 years?"
  • "Why do you want to work with Professor [name] specifically?"
4.4 Behavioral Questions
  • "Tell me about a research challenge you overcame."
  • "Describe a time you collaborated on a team project."
  • "Tell me about a time your experiment failed. What did you learn?"
  • "Describe a situation where you had to learn a new skill quickly."
4.5 Project Deep-dive Questions

Based on specific projects listed in the user's CV:

  • "Can you walk me through your [project name] project?"
  • "What was the most challenging aspect of [project]?"
  • "If you could redo [project], what would you change?"
  • "How did you validate your results in [project]?"

5. Conduct Mock Q&A

Format
  1. Present one question at a time
  2. Wait for the user's response
  3. Provide constructive feedback on the response, including:
  • What was strong about the answer
  • What could be improved
  • Suggested structure or content additions
  • Tips for delivering the answer in an actual interview
  1. Then present the next question
Progression
  • Start with easier questions (motivation, background)
  • Move to more challenging questions (technical, research depth)
  • End with behavioral questions (typically shorter answers)
Tracking

Keep track of:

  • Questions answered and feedback given
  • Patterns in the user's strengths and weaknesses
  • Time spent per question category

6. Post-Practice Summary

After completing the mock Q&A, provide a structured summary:

## Interview Prep Summary — [Professor Name], [University]
### Strengths
- [strength 1]
- [strength 2]
- [strength 3]
### Areas to Improve
- [area 1]: [specific improvement suggestion]
- [area 2]: [specific improvement suggestion]
### Key Points to Remember
- [important point about professor's research]
- [important point about program expectations]
### Recommended Preparation
- [preparation task 1]
- [preparation task 2]
- [preparation task 3]
### Questions to Ask the Professor
- [suggested question 1]
- [suggested question 2]
- [suggested question 3]

Security

  • Security scanPassed, 95 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
66
Repository forks
2
7-day trend score
21

Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.

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