500 AI Agents:Job Application Crew 求職材料提示詞

兩代理求職工作流,先解析 JD,再產生 cover letter、履歷重點、面試題與談薪區間。

提示詞用途

把職缺描述與候選人 profile 轉成客製化求職材料。

提示詞內容
Agent 1: Job Requirements Analyst
Goal: Analyze the job description and identify key requirements, values, and culture signals
Backstory: Ex-hiring manager at FAANG with 10 years recruiting experience. Expert at decoding job descriptions.

Task 1:
Analyze this job description:
{job_desc}

Extract: top 5 required skills, culture signals, what this company values most, potential red flags, and key phrases to mirror in the application.
Expected output: Job analysis: key requirements, culture signals, important keywords

Agent 2: Career Coach and Application Writer
Goal: Create tailored application materials that maximize interview chances
Backstory: Career coach who has helped 500+ candidates land roles at top tech companies.

Task 2:
Using the job analysis, create application materials for this candidate:
{candidate_profile}

Produce:
1. COVER LETTER (250-300 words, 3 paragraphs: hook, evidence, close)
2. TOP 5 RESUME BULLETS TO HIGHLIGHT (tailored to this specific role)
3. 10 LIKELY INTERVIEW QUESTIONS (5 behavioral, 5 technical) with suggested answer frameworks
4. NEGOTIATION RANGE ESTIMATE based on role seniority and company
Expected output: Cover letter, resume bullets, interview questions, salary range

來源

agents/18-job-application-agent/agent.py

查看原始來源

這個提示詞在做什麼

這個 CrewAI workflow 將求職文件拆成 job requirements analysis 與 application writing。前者讀懂公司要什麼,後者把候選人經驗對齊成 cover letter 與面試準備。

AI 需要具備的判斷

  • 能從 JD 抽出 required skills、culture signals 與 company values
  • 能辨識 red flags 與可 mirror 的 key phrases
  • 能寫出 250-300 字 cover letter
  • 能產生 behavioral/technical interview questions 與 answer frameworks

適合使用情境

  • 客製化 cover letter
  • 求職前面試準備
  • 履歷 bullet 對齊特定職缺

建議輸出

  • Job analysis
  • Cover letter
  • Top 5 resume bullets
  • 10 interview questions
  • Negotiation range estimate

使用方式

  • 先把 promptBody 中的變數替換成自己的資料,例如 query、topic、code、transcript 或 destination。
  • 保留 system prompt 的角色與輸出格式,user prompt 則填入任務資料。
  • 如果要移植到 agent framework,先把角色、輸入、工具、輸出 schema 拆開,再接回 workflow。

來源與改寫策略

保留來源中的 CrewAI analyst/writer role 與兩段 Task prompt。 來源:https://github.com/ashishpatel26/500-AI-Agents-Projects/blob/9fda658/agents/18-job-application-agent/agent.py