Task Router Skill
Task Router
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.
Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.
SkillHub CLI
skillhub install agistack/interviewExaminez la source et la compatibilité, puis suivez les étapes d’installation.
Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.
Interview mastery system. Preparation that wins offers.
memory/interview/
Interview data stored locally:
memory/interview/research.json - Company research briefsmemory/interview/stories.json - Story librarymemory/interview/practice.json - Mock interview recordsmemory/interview/salary.json - Salary research and strategiesmemory/interview/feedback.json - Post-interview notesUser: "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
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
User: "Run a mock interview for PM role"
→ Use scripts/mock_interview.py --role "Product Manager" --level senior
→ Ask realistic questions, provide honest feedback
User: "How should I handle the salary question?"
→ Use scripts/prep_salary.py --role "Product Manager" --location "SF"
→ Research market data, prepare negotiation strategy
User: "Draft thank you email for today's interview"
→ Use scripts/draft_followup.py --interview "INT-123" --tone...
Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.
# 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...
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