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Interview Mock Partner

该 Skill 面向医疗行业求职者,按目标职位、经验水平和专科方向生成行为面试问题,并提供参考答案与改进建议。输出为 JSON,可打印到标准输出或写入指定文件;材料称无外部 API 调用,需本地 Python 运行。

aipoch-ai
aipoch-ai
热度 34.76
最近更新 2026-05-11 17:36:19
分类:知识依赖:代码执行 / 读写文件应用场景:教学辅导

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add interview-mock-partner
Metadata
name
interview-mock-partner
description
Simulates behavioral interview questions for medical professionals.
version
1.0.0
category
Career
tags
["interview","mock","behavioral","career"]
author
AIPOCH
license
MIT
status
Draft
risk_level
Medium
skill_type
Tool/Script
owner
AIPOCH
reviewer
last_updated
2026-02-06

Interview Mock Partner

Simulates medical job interview scenarios.

Features

  • Behavioral questions
  • Response feedback
  • Common scenarios
  • Improvement tips

Parameters

Parameter Type Default Required Description
--position string - Yes Target position title
--experience-level string entry No Experience level (entry, mid, senior)
--specialty string - No Medical specialty area
--questions int 5 No Number of questions to generate
--output, -o string stdout No Output file path

Output Format

{
  "questions": ["string"],
  "sample_answers": ["string"],
  "tips": ["string"]
}

Risk Assessment

Risk Indicator Assessment Level
Code Execution Python/R scripts executed locally Medium
Network Access No external API calls Low
File System Access Read input files, write output files Medium
Instruction Tampering Standard prompt guidelines Low
Data Exposure Output files saved to workspace Low

Security Checklist

  • No hardcoded credentials or API keys
  • No unauthorized file system access (../)
  • Output does not expose sensitive information
  • Prompt injection protections in place
  • Input file paths validated (no ../ traversal)
  • Output directory restricted to workspace
  • Script execution in sandboxed environment
  • Error messages sanitized (no stack traces exposed)
  • Dependencies audited

Prerequisites

No additional Python packages required.

Evaluation Criteria

Success Metrics

  • Successfully executes main functionality
  • Output meets quality standards
  • Handles edge cases gracefully
  • Performance is acceptable

Test Cases

  1. Basic Functionality: Standard input → Expected output
  2. Edge Case: Invalid input → Graceful error handling
  3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

  • Current Stage: Draft
  • Next Review Date: 2026-03-06
  • Known Issues: None
  • Planned Improvements:
    • Performance optimization
    • Additional feature support