interview

name	interview
description	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.

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
• Story Building: See references/stories.md
• Mock Interviews: See references/mock-interviews.md
• Salary Negotiation: See references/salary.md
• Difficult Questions: See references/difficult-questions.md
• Follow-up Strategy: See references/followup.md
• Handling Rejection: See 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