interview-mock-partner

by aipoch-ai

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SKILL.md About

---
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

```json
{
  "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

_meta.json Metadata

{
  "ownerId": "kn76fcc8jhxekae91m2ekbrsgn821avs",
  "publishedAt": 1774425175370,
  "slug": "interview-mock-partner",
  "version": "1.0.0"
}