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.

Safety Notice

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Install skill "interview" with this command: npx skills add agistack/interview

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

Scripts Reference

ScriptPurpose
research_company.pyGenerate company research brief
build_story.pyBuild STAR format stories
mock_interview.pyRun practice interview
prep_salary.pyPrepare salary strategy
draft_followup.pyDraft follow-up messages
analyze_role.pyAnalyze job description
identify_gaps.pyIdentify experience gaps
log_feedback.pyLog post-interview feedback

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

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