Agent skills
linkedin_job_resume_optimizer

Agent skill

linkedin_job_resume_optimizer

Automates end-to-end job search workflow: searches LinkedIn for AI-related remote jobs, extracts job descriptions, reads base resume, generates ATS-optimized tailored resumes, performs skill gap analysis, suggests interview prep questions, and provides LinkedIn profile optimization recommendations. Use when: (1) User needs job-specific resume tailoring, (2) Preparing for job applications with targeted optimization, (3) Requires skill gap analysis between resume and job requirements, (4) Needs interview preparation questions based on skill gaps.

View SKILL.md on GitHub Repository

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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/linkedin-job-resume-optimizer

SKILL.md

LinkedIn Job Resume Optimizer

Comprehensive job search automation with resume tailoring and interview preparation.

Overview

This skill orchestrates a complete job application workflow:

Search LinkedIn for relevant remote AI jobs
Extract full job descriptions and requirements
Analyze base resume to extract skills and experience
Generate ATS-optimized, job-tailored resumes
Perform skill gap analysis
Generate targeted interview preparation questions
Suggest LinkedIn profile optimizations

Workflow Diagram

User Request
    ↓
Workflow Orchestrator
    ↓
┌─────────────────┬──────────────────┬─────────────────┐
│ LinkedIn        │ Resume           │ ATS             │
│ Scraper         │ Analyzer         │ Optimizer       │
│ (Playwright)    │ (Pandoc+Docx)    │ (NLP)           │
└─────────────────┴──────────────────┴─────────────────┘
    ↓
Gap Analyzer (Compare skills)
    ↓
Question Generator (Interview prep)
    ↓
Final Report (Summary + File locations)

Prerequisites

1. Playwright MCP Server (for LinkedIn automation)

Start the server before running this skill:

bash

bash /mnt/d/Ali_Home/Learning/AgenticAI/AI-P009/Assignments/ProjectA1/resume_optimizer/.claude/skills/browsing-with-playwright/scripts/start-server.sh

2. Base Resume

Ensure resume exists at configured path (default: /mnt/d/Ali_Home/Learning/AgenticAI/AI-P009/Assignments/ProjectA1/resume_optimizer/resume/resume06012026.docx)

3. System Dependencies

bash

# Core tools
sudo apt-get install pandoc python3 python3-pip nodejs npm

# Optional (for PDF export)
sudo apt-get install libreoffice poppler-utils

4. Python Packages

bash

pip install python-docx spacy nltk pandas requests
python -m spacy download en_core_web_sm
python -m nltk.downloader punkt stopwords

5. Node Packages

bash

npm install -g @playwright/mcp

Quick Start

Basic Usage

bash

python3 scripts/workflow_orchestrator.py \
  --job-keywords "AI Engineer remote" \
  --job-count 2

With Custom Resume Path

bash

python3 scripts/workflow_orchestrator.py \
  --resume-path "/path/to/your/resume.docx" \
  --job-keywords "Machine Learning Engineer" \
  --job-count 2 \
  --output-dir "./custom_output"

Step-by-Step Workflow

Phase 1: Initialize Playwright Server

bash

bash /mnt/d/Ali_Home/Learning/AgenticAI/AI-P009/Assignments/ProjectA1/resume_optimizer/.claude/skills/browsing-with-playwright/scripts/start-server.sh

Verify server is running:

bash

curl http://localhost:8808

Phase 2: Search LinkedIn Jobs

bash

python3 scripts/linkedin_scraper.py \
  --keywords "AI Engineer remote" \
  --count 2 \
  --output jobs.json

Output: jobs.json containing:

json

[
  {
    "title": "Senior AI Engineer",
    "company": "TechCorp",
    "url": "https://linkedin.com/jobs/view/12345",
    "description": "Full job description...",
    "required_skills": ["Python", "TensorFlow", "MLOps"],
    "preferred_skills": ["Kubernetes", "AWS"]
  }
]

Phase 3: Extract Base Resume Skills

bash

# Convert DOCX to markdown
pandoc --track-changes=accept /mnt/d/Ali_Home/Learning/AgenticAI/AI-P009/Assignments/ProjectA1/resume_optimizer/resume/resume06012026.docx -o resume.md

# Extract skills
python3 scripts/resume_analyzer.py --resume-md resume.md --output base_skills.json

Output: base_skills.json with skills inventory

Phase 4: Generate Tailored Resumes

For each job, generate ATS-optimized resume:

bash

python3 scripts/ats_optimizer.py \
  --base-resume /mnt/d/Ali_Home/Learning/AgenticAI/AI-P009/Assignments/ProjectA1/resume_optimizer/resume/resume06012026.docx \
  --job-description jobs.json \
  --job-index 0 \
  --output "resume_tailored_Senior_AI_Engineer.docx"

Phase 5: Perform Gap Analysis

bash

python3 scripts/gap_analyzer.py \
  --base-skills base_skills.json \
  --job-requirements jobs.json \
  --output gap_analysis.json

Output: gap_analysis.json

json

[
  {
    "job_title": "Senior AI Engineer",
    "skill_gaps": [
      {
        "skill": "Kubernetes",
        "gap_type": "missing",
        "importance": "high",
        "related_experience": ["Docker experience in current role"]
      }
    ],
    "interview_questions": [
      "Explain your understanding of Kubernetes orchestration...",
      "How would you design a Kubernetes deployment for ML models?",
      "..."
    ],
    "linkedin_updates": {
      "about_section": "Add: 'Experienced in containerization with Docker, transitioning to Kubernetes for production ML deployments'",
      "skills_to_add": ["Kubernetes", "Container Orchestration", "MLOps"]
    }
  }
]

Phase 6: Generate Interview Questions

bash

python3 scripts/question_generator.py \
  --gap-analysis gap_analysis.json \
  --questions-per-gap 10 \
  --output interview_prep.md

Phase 7: Cleanup

bash

bash /mnt/d/Ali_Home/Learning/AgenticAI/AI-P009/Assignments/ProjectA1/resume_optimizer/.claude/skills/browsing-with-playwright/scripts/stop-server.sh

Configuration

Edit config.json in skill directory:

json

{
  "resume_path": "/mnt/d/Ali_Home/Learning/AgenticAI/AI-P009/Assignments/ProjectA1/resume_optimizer/resume/resume06012026.docx",
  "output_directory": "./resume_optimizer/output",
  "playwright_port": 8808,
  "job_search": {
    "default_keywords": "AI Engineer remote",
    "default_count": 2,
    "location": "Remote"
  },
  "ats_optimization": {
    "keyword_density_target": 0.75,
    "exact_match_priority": true,
    "preserve_formatting": true
  },
  "interview_prep": {
    "questions_per_gap": 10,
    "include_behavioral": true,
    "include_technical": true
  }
}

ATS Optimization Strategies

See references/ats-optimization-guide.md for detailed strategies including:

Keyword Density

Required Skills: 70-80% coverage
Preferred Skills: 40-50% coverage
Exact Match: Use exact phrases from job description
Context Integration: Weave keywords naturally into experience descriptions

Keyword Placement Priority

Professional Summary (highest ATS weight)
- Include job title keyword
- Top 3-5 required skills
- Industry-specific terminology
Skills Section (exact match critical)
- Mirror job posting's skills terminology exactly
- Group by category if job posting does
Experience Descriptions (context + keywords)
- Integrate keywords into achievement statements
- Use action verbs + keyword + quantifiable result

Natural Integration Techniques

Action Verb + Keyword + Result:

Before: "Worked on machine learning projects"
After: "Developed TensorFlow-based ML models, reducing inference time by 30%"

Technology Stack Enumeration:

Before: "Built backend systems"
After: "Built scalable backend systems using Python, Docker, and Kubernetes on AWS infrastructure"

LinkedIn Automation

See references/linkedin-automation.md for:

Navigation patterns for LinkedIn job search
Element selectors and snapshot references
Rate limiting and anti-bot detection strategies
Fallback strategies if elements change

Key Points:

Implement random delays (1-3 seconds between actions)
Use --shared-browser-context flag for Playwright MCP
Handle rate limiting with exponential backoff
Parse snapshots for job card refs

Interview Question Generation

See references/interview-prep-patterns.md for:

STAR method question templates
Technical depth assessment questions
Behavioral questions for skill gaps
System design scenarios

Question Distribution:

Missing Skills: 40% fundamentals, 30% transferable experience, 30% scenarios
Weak Skills: 40% depth assessment, 30% practical application, 30% troubleshooting

Error Handling

No Jobs Found

python

if len(jobs) == 0:
    print("No jobs found matching criteria. Suggestions:")
    print("- Broaden search keywords")
    print("- Remove 'remote' filter")
    print("- Try alternative job titles")
    sys.exit(1)

Resume Read Errors

python

try:
    resume_text = extract_resume(resume_path)
except FileNotFoundError:
    print(f"Resume not found at {resume_path}")
    print("Please provide valid resume path with --resume-path")
    sys.exit(1)
except Exception as e:
    print(f"Error reading resume: {e}")
    print("Ensure resume is valid .docx format")
    sys.exit(1)

LinkedIn Rate Limiting

python

# Implement exponential backoff
for attempt in range(3):
    try:
        job_data = scrape_job(url)
        break
    except RateLimitError:
        wait_time = 2 ** attempt * 5  # 5s, 10s, 20s
        time.sleep(wait_time)

Playwright Connection Failures

Pre-flight check: Verify server running before job search
Auto-restart server if connection fails
Retry with exponential backoff (3 attempts)

Troubleshooting

Issue	Solution
Playwright not responding	Restart: `bash scripts/stop-server.sh && bash scripts/start-server.sh`
LinkedIn blocks automation	Add wait times, use headless:false, rotate user agents
Resume conversion fails	Verify pandoc installed: `pandoc --version`
ATS optimization weak	Review keyword extraction, increase density target in config.json
No skill gaps detected	Lower similarity threshold in gap_analyzer.py
Python packages missing	Run: `pip install python-docx spacy nltk pandas requests`
spaCy model not found	Run: `python -m spacy download en_core_web_sm`

Output Summary Format

After running the workflow, you'll receive a summary report:

markdown

# Job Search Results Summary

## Jobs Found: 2

### Job 1: Senior AI Engineer - TechCorp
- **LinkedIn URL**: https://linkedin.com/jobs/view/12345
- **Tailored Resume**: ./resume_optimizer/output/resume_tailored_Senior_AI_Engineer.docx
- **Key Requirements**: Python, TensorFlow, MLOps, Kubernetes
- **Skill Gaps**: Kubernetes (High priority), AWS Sagemaker (Medium)
- **Interview Prep Questions**: 20 questions generated (see interview_prep.md)

### Job 2: Machine Learning Engineer - AI Startup
- **LinkedIn URL**: https://linkedin.com/jobs/view/67890
- **Tailored Resume**: ./resume_optimizer/output/resume_tailored_Machine_Learning_Engineer.docx
- **Key Requirements**: PyTorch, Docker, CI/CD, Model deployment
- **Skill Gaps**: PyTorch (High priority - similar TensorFlow experience)
- **Interview Prep Questions**: 15 questions generated (see interview_prep.md)

## LinkedIn Profile Recommendations

### About Section Updates
- Add: "Specialized in MLOps with focus on production-scale deployments"
- Emphasize: Kubernetes orchestration experience
- Highlight: Cross-functional collaboration in AI product development

### Skills to Add
1. Kubernetes (High priority - both jobs)
2. MLOps (Critical keyword)
3. Model Deployment
4. CI/CD for ML

## Next Steps
1. Review tailored resumes in ./resume_optimizer/output/
2. Study interview prep questions in interview_prep.md
3. Update LinkedIn profile per recommendations above
4. Apply to jobs with tailored resumes

Integration with Existing Skills

This skill leverages:

browsing-with-playwright

LinkedIn job search automation
Browser navigation and snapshot parsing
Start server: bash scripts/start-server.sh
Stop server: bash scripts/stop-server.sh

docx

Resume reading using pandoc
Tailored resume generation
Convert: pandoc --track-changes=accept resume.docx -o resume.md

Advanced Usage

Manual Job URLs (Skip LinkedIn Scraping)

If you prefer to provide job URLs manually:

Create jobs_manual.json:

json

[
  {
    "title": "AI Engineer",
    "company": "CompanyName",
    "url": "https://linkedin.com/jobs/view/12345",
    "description": "Paste full job description here..."
  }
]

Run workflow with manual jobs:

bash

python3 scripts/workflow_orchestrator.py \
  --manual-jobs jobs_manual.json \
  --skip-linkedin

Custom Keyword Extraction

Override automatic skill extraction:

bash

python3 scripts/ats_optimizer.py \
  --base-resume resume.docx \
  --job-description jobs.json \
  --job-index 0 \
  --custom-keywords "Python,TensorFlow,Kubernetes,AWS" \
  --output tailored_resume.docx

Dependencies Summary

Required

Python 3.8+
Node.js 14+
pandoc
browsing-with-playwright skill
docx skill

Python Packages

python-docx
spacy
nltk
pandas
requests

Node Packages

@playwright/mcp

Performance

LinkedIn Search: 30-90 seconds (2 jobs)
Resume Analysis: 10-20 seconds
Resume Tailoring: 30-60 seconds per job
Gap Analysis: 20-30 seconds
Interview Prep: 40-60 seconds
Total Estimated Time: 2-4 minutes for 2 jobs

Limitations

LinkedIn Access: Requires LinkedIn to be accessible (may need login for some content)
Job Count: Recommended maximum 5 jobs per session to avoid rate limiting
Resume Format: Only supports .docx format (not .doc or PDF)
Language: Optimized for English resumes and job descriptions
Technical Roles: Best suited for technical roles (AI, ML, Engineering)

Security & Privacy

Local Processing: All resume processing happens locally
No Storage: No resume data is stored remotely
Browser Automation: Uses local Playwright instance
Data Privacy: Job descriptions and resumes remain on your machine

License

This skill is part of the Claude Code skills ecosystem.

Maintainer

majiayu000 Core maintainer

Source details

Full Name: majiayu000/claude-skill-registry
Branch: main
Path in repo: skills/data/linkedin-job-resume-optimizer
License: MIT License

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Install this agent skill to your Project

SKILL.md

LinkedIn Job Resume Optimizer

Overview

Workflow Diagram

Prerequisites

1. Playwright MCP Server (for LinkedIn automation)

2. Base Resume

3. System Dependencies

4. Python Packages

5. Node Packages

Quick Start

Basic Usage

With Custom Resume Path

Step-by-Step Workflow

Phase 1: Initialize Playwright Server

Phase 2: Search LinkedIn Jobs

Phase 3: Extract Base Resume Skills

Phase 4: Generate Tailored Resumes

Phase 5: Perform Gap Analysis

Phase 6: Generate Interview Questions

Phase 7: Cleanup

Configuration

ATS Optimization Strategies

Keyword Density

Keyword Placement Priority

Natural Integration Techniques

LinkedIn Automation

Interview Question Generation

Error Handling

No Jobs Found

Resume Read Errors

LinkedIn Rate Limiting

Playwright Connection Failures

Troubleshooting

Output Summary Format

Integration with Existing Skills

browsing-with-playwright

docx

Advanced Usage

Manual Job URLs (Skip LinkedIn Scraping)

Custom Keyword Extraction

Dependencies Summary

Required

Python Packages

Node Packages

Performance

Limitations

Security & Privacy

License

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