interview-mock-partner
Simulates behavioral interview questions for medical professionals.
Best use case
interview-mock-partner is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Simulates behavioral interview questions for medical professionals.
Teams using interview-mock-partner should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/interview-mock-partner/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How interview-mock-partner Compares
| Feature / Agent | interview-mock-partner | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Simulates behavioral interview questions for medical professionals.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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 supportRelated Skills
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