AgentSkillsCN

cover-letter-generator

运用PSI（问题—解决方案—影响）方法论，量身定制专注于AI领域的求职信。 适用场景如下：(1) 用户希望为AI/ML岗位的求职申请撰写求职信；(2) 用户已准备好简历，希望借助LinkedIn进行职位匹配；(3) 用户根据招聘岗位的需求，寻求个性化的求职信撰写服务；(4) 用户明确表示自己正申请AI工程师、机器学习工程师等技术类岗位。 整合市场情报、通过Playwright进行LinkedIn深度调研，并遵循专业写作规范。

SKILL.md
--- frontmatter
name: cover-letter-generator
description: |
  Generate tailored AI-focused cover letters using the PSI (Problem-Solution-Impact) methodology.
  Use when: (1) User wants to create cover letters for AI/ML job applications, (2) User provides a resume and wants LinkedIn job matching, (3) User asks for personalized cover letters based on job postings, (4) User mentions applying for AI Engineer, ML Engineer, or similar technical roles.
  Integrates market intelligence, LinkedIn research via Playwright, and professional writing standards.

Cover Letter Generator

Generate PSI-formatted cover letters tailored to LinkedIn AI job postings.

Workflow

Step 1: Analyze Resume

Extract and analyze the applicant's resume:

bash
python3 scripts/extract_resume.py "<path_to_resume.docx>"

Identify from the resume:

  • •Core technical stack: Languages, frameworks, platforms
  • •Quantified achievements: Metrics, percentages, business outcomes
  • •Domain experience: Industries, project types, team sizes
  • •AI/ML specific skills: Models, pipelines, tools

Step 2: Market Intelligence

Based on the resume profile, identify the top 3 AI skills currently in demand:

Common high-demand AI skills (2024-2025):

  • •RAG (Retrieval-Augmented Generation) pipelines
  • •Agentic AI workflows (LangGraph, AutoGen, CrewAI)
  • •LLMOps / MLOps (deployment, monitoring, fine-tuning)
  • •Prompt engineering & context optimization
  • •Vector databases & semantic search
  • •Multi-modal AI systems

Match resume skills to market demand to identify positioning strategy.

Step 3: LinkedIn Research

Use browsing-with-playwright skill or Playwright MCP to search LinkedIn for relevant jobs:

Search Strategy:

  1. •Navigate to LinkedIn Jobs: https://www.linkedin.com/jobs/
  2. •Search terms combining: [Primary Skill] + [Secondary Skill] + [Location/Remote]
    • •Example: "Agentic AI Developer Remote"
    • •Example: "RAG Engineer LLMOps"
  3. •Find 2 relevant job postings matching the profile
  4. •For each job, extract:
    • •Company name and job title
    • •Key technical requirements
    • •Company's AI focus/challenges (from description)
    • •Hiring manager name (if visible)

Step 4: Bridge the Capability Gap

For each job posting, create a PSI mapping:

ComponentSourceAction
ProblemJob postingIdentify the organization's technical bottleneck
SolutionResumeMap applicant's skills as the solution
ImpactResumeExtract metrics proving ROI capability

Constraint: Never fabricate experience. Reframe existing resume data to address the job's specific challenges.

Step 5: Generate Cover Letters

Create 2 cover letters using the PSI template. See references/psi_template.md.

Requirements:

  • •Follow PSI format strictly (Problem -> Solution -> Impact)
  • •Integrate all 5 quality pillars from references/quality_pillars.md
  • •Maintain professional, technical, impact-oriented tone
  • •Ensure "Translation Layer" is evident (explaining AI to stakeholders)
  • •Hyper-personalize to each company's context

Quality Checklist:

  • • Problem identifies company's specific AI challenge
  • • Solution uses concrete tools/methods from resume
  • • Impact includes quantified metrics
  • • Mentions Responsible AI / ethics alignment
  • • Demonstrates learning velocity (current tech awareness)
  • • References company-specific information
  • • Written with clarity for non-technical readers

Output Format

Deliver 2 complete cover letters, each with:

  1. •Header (name, LinkedIn, GitHub)
  2. •Subject line targeting company's challenge
  3. •PSI body paragraphs
  4. •Professional closing

Save as: cover_letter_[company_name].md