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:
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:
- Navigate to LinkedIn Jobs:
https://www.linkedin.com/jobs/ - Search terms combining:
[Primary Skill] + [Secondary Skill] + [Location/Remote]- Example: "Agentic AI Developer Remote"
- Example: "RAG Engineer LLMOps"
- Find 2 relevant job postings matching the profile
- 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:
| Component | Source | Action | |-----------|--------|--------| | Problem | Job posting | Identify the organization's technical bottleneck | | Solution | Resume | Map applicant's skills as the solution | | Impact | Resume | Extract 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:
- Header (name, LinkedIn, GitHub)
- Subject line targeting company's challenge
- PSI body paragraphs
- Professional closing
Save as: cover_letter_[company_name].md