interview
Interview preparation system with company research, story building, and mock interview practice. Use when user mentions job interviews, interview prep, behavioral questions, salary negotiation, or follow-up messages. Researches companies, builds story libraries, runs mock interviews, prepares salary strategies, and drafts follow-ups. NEVER guarantees job offers.
Best use case
interview is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Interview preparation system with company research, story building, and mock interview practice. Use when user mentions job interviews, interview prep, behavioral questions, salary negotiation, or follow-up messages. Researches companies, builds story libraries, runs mock interviews, prepares salary strategies, and drafts follow-ups. NEVER guarantees job offers.
Teams using interview 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/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How interview Compares
| Feature / Agent | interview | 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?
Interview preparation system with company research, story building, and mock interview practice. Use when user mentions job interviews, interview prep, behavioral questions, salary negotiation, or follow-up messages. Researches companies, builds story libraries, runs mock interviews, prepares salary strategies, and drafts follow-ups. NEVER guarantees job offers.
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 Interview mastery system. Preparation that wins offers. ## Critical Privacy & Safety ### Data Storage (CRITICAL) - **All interview data stored locally only**: `memory/interview/` - **No external job platforms** connected - **No application tracking systems** integrated - **No sharing** of interview content - User controls all data retention and deletion ### Safety Boundaries - ✅ Research companies and roles - ✅ Build story libraries from experience - ✅ Run mock interviews with feedback - ✅ Prepare salary negotiation strategies - ❌ **NEVER guarantee** job offers - ❌ **NEVER provide** false information - ❌ **NEVER replace** genuine preparation ### Data Structure Interview data stored locally: - `memory/interview/research.json` - Company research briefs - `memory/interview/stories.json` - Story library - `memory/interview/practice.json` - Mock interview records - `memory/interview/salary.json` - Salary research and strategies - `memory/interview/feedback.json` - Post-interview notes ## Core Workflows ### Research Company ``` User: "Research Acme Corp for my interview Friday" → Use scripts/research_company.py --company "Acme Corp" --role "Product Manager" → Generate comprehensive research brief with talking points ``` ### Build Story ``` User: "Help me build a story about the project failure" → Use scripts/build_story.py --situation "project-failure" --lesson "learned" → Structure STAR format story with specific details ``` ### Mock Interview ``` User: "Run a mock interview for PM role" → Use scripts/mock_interview.py --role "Product Manager" --level senior → Ask realistic questions, provide honest feedback ``` ### Prepare Salary ``` User: "How should I handle the salary question?" → Use scripts/prep_salary.py --role "Product Manager" --location "SF" → Research market data, prepare negotiation strategy ``` ### Draft Follow-up ``` User: "Draft thank you email for today's interview" → Use scripts/draft_followup.py --interview "INT-123" --tone professional → Generate specific, memorable follow-up message ``` ## Module Reference - **Company Research**: See [references/research.md](references/research.md) - **Story Building**: See [references/stories.md](references/stories.md) - **Mock Interviews**: See [references/mock-interviews.md](references/mock-interviews.md) - **Salary Negotiation**: See [references/salary.md](references/salary.md) - **Difficult Questions**: See [references/difficult-questions.md](references/difficult-questions.md) - **Follow-up Strategy**: See [references/followup.md](references/followup.md) - **Handling Rejection**: See [references/rejection.md](references/rejection.md) ## Scripts Reference | Script | Purpose | |--------|---------| | `research_company.py` | Generate company research brief | | `build_story.py` | Build STAR format stories | | `mock_interview.py` | Run practice interview | | `prep_salary.py` | Prepare salary strategy | | `draft_followup.py` | Draft follow-up messages | | `analyze_role.py` | Analyze job description | | `identify_gaps.py` | Identify experience gaps | | `log_feedback.py` | Log post-interview feedback |
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