interview-prep

Generate interview question bank and answer strategy from JD and company intel.

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Install skill "interview-prep" with this command: npx skills add wanghong5233/wanghong5233-offerpilot-interview-prep

Interview Prep Skill

Trigger

Activate when user asks:

  • "帮我准备这家公司的面试题"
  • "根据 JD 出一套面试问题"
  • "给我这岗位的回答思路"
  • "做一版可背诵的面试提纲"

Workflow

  1. Collect input:
    • Prefer job_id (from /api/jobs/recent) OR provide company + role_title + jd_text.
  2. Call:
    • POST http://127.0.0.1:8010/api/interview/prep
    • Body example:
      • {"job_id":"<job_id>","use_company_intel":true,"question_count":8}
    • Or:
      • {"company":"MiniAgent","role_title":"AI Agent Intern","jd_text":"...","use_company_intel":true,"question_count":8}
  3. Parse response and present:
    • summary
    • likely_focus
    • key_storylines
    • top interview questions (question, intent, answer_tips)
  4. Ask user whether to export/continue with mock Q&A.

Command templates (exec tool + curl)

  • By job id:
    • curl -sS -X POST "http://127.0.0.1:8010/api/interview/prep" -H "Content-Type: application/json" -d '{"job_id":"<job_id>","use_company_intel":true,"question_count":8}'
  • By custom input:
    • curl -sS -X POST "http://127.0.0.1:8010/api/interview/prep" -H "Content-Type: application/json" -d '{"company":"MiniAgent","role_title":"AI Agent Intern","jd_text":"Need Python, LangGraph, RAG","use_company_intel":true,"question_count":8}'

Constraints

  • Keep output concise and actionable (avoid long generic theory).
  • If API returns non-2xx, surface the raw error and ask user whether to retry.
  • Do not claim interview certainty; present as "likely focus" with confidence.

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