LinkedIn Job Search Skill

Search and monitor LinkedIn job listings with powerful filters. Supports 100+ global tech hubs with precise geo IDs, hourly monitoring via cron, and smart deduplication.

Configuration

After installation, optionally customize by copying config.example.json to config.json:

cp {baseDir}/config.example.json {baseDir}/config.json

Configurable options:

• Default filters (experience, remote, date_posted)
• Scraper delays and timeout
• Notification preferences
• Custom geo IDs for your cities

Capabilities

1. One-time search: Search LinkedIn for jobs matching keywords and filters
2. Scheduled monitoring: Add search profiles that run hourly via cron
3. Smart deduplication: Only shows new jobs you haven't seen before
4. Global city support: 100+ tech hubs with precise geo IDs

How to Use

One-Time Job Search

Use the exec tool to run a direct search:

python {baseDir}/linkedin_scraper.py --keywords "AI Engineer" --location "Bengaluru, India" --max-pages 2

Parameters:

Example - Entry level AI jobs in Noida, hybrid/on-site:

python {baseDir}/linkedin_scraper.py --keywords "AI Engineer" --location "Noida, India" --experience "2" --remote "1,3" --max-pages 2

Managing Search Profiles (for Hourly Monitoring)

When the user wants to set up recurring job searches, use these commands:

Add a new search profile:

python {baseDir}/linkedin_cron.py add --keywords "AI Engineer" --location "Bengaluru, India"

Add multiple job titles at once (comma-separated):

python {baseDir}/linkedin_cron.py add --keywords "AI Engineer, ML Engineer, Data Scientist" --location "Bengaluru, India"

This creates 3 separate profiles with the same location and filters, and deduplicates results across all of them.

Add with custom filters:

python {baseDir}/linkedin_cron.py add --keywords "Python Developer" --location "San Francisco" --experience "2" --remote "2,3"

List all search profiles:

python {baseDir}/linkedin_cron.py list

Run all enabled profiles now (for hourly cron or manual check):

python {baseDir}/linkedin_cron.py run

Run specific profile:

python {baseDir}/linkedin_cron.py run --profile ai-engineer-bengaluru

Enable/Disable a profile:

python {baseDir}/linkedin_cron.py enable --profile ai-engineer-bengaluru
python {baseDir}/linkedin_cron.py disable --profile ai-engineer-bengaluru

Remove a profile:

python {baseDir}/linkedin_cron.py remove --profile ai-engineer-bengaluru

Clear job history (to see all jobs again):

python {baseDir}/linkedin_cron.py clear-history

View statistics:

python {baseDir}/linkedin_cron.py stats

Supported Cities (100+ Global Tech Hubs)

The skill has built-in geo IDs for precise location-based results:

India: Bengaluru, Noida, Hyderabad, Mumbai, Delhi NCR, Pune, Chennai, Gurugram, Kolkata, Ahmedabad, Jaipur, Chandigarh, Kochi, Coimbatore, Indore, Lucknow

USA: San Francisco, New York, Seattle, Austin, Boston, Los Angeles, Chicago, Denver, San Diego, Washington DC, Atlanta, Dallas, Houston, Phoenix, Miami, Portland

UK: London, Manchester, Edinburgh, Cambridge, Oxford, Bristol, Birmingham, Leeds, Glasgow

Europe: Berlin, Amsterdam, Dublin, Paris, Munich, Zurich, Stockholm, Barcelona, Madrid, Milan, Vienna, Prague, Warsaw, Brussels, Copenhagen

Asia Pacific: Singapore, Sydney, Melbourne, Tokyo, Hong Kong, Seoul, Taipei, Kuala Lumpur, Jakarta, Bangkok, Shanghai, Beijing

Canada: Toronto, Vancouver, Montreal, Ottawa, Calgary, Waterloo

Middle East: Dubai, Abu Dhabi, Riyadh, Tel Aviv, Doha

Latin America: Sao Paulo, Mexico City, Buenos Aires, Bogota, Santiago

Africa: Johannesburg, Cape Town, Lagos, Nairobi, Cairo

For unlisted cities, the skill falls back to text-based search. You can also add custom geo IDs in config.json.

Output Format

The scraper returns JSON with job details including:

• title: Job title
• company: Company name
• location: Job location
• employment_type: Full-time, Part-time, Contract, etc.
• experience_level: Entry level, Mid-Senior, etc.
• posted_date: When the job was posted
• requirements: Experience requirements extracted from description
• tech_stack: Technologies mentioned (Python, TensorFlow, AWS, etc.)
• role_summary: Brief description of the role
• url: Direct link to apply

Formatting Job Notifications

When presenting new jobs to the user, format them clearly:

Found X new jobs for "[keywords]":

━━━ [Location] ━━━

1. [Title] @ [Company]
   📍 [Location] ([Remote/Hybrid/On-site])
   💼 [Employment Type] | [Experience Level]
   🕐 Posted [time ago]
   
   📋 Requirements:
   • Experience: [requirements]
   • Tech Stack: [tech_stack]
   • Role: [role_summary]
   
   🔗 [url]

User Intent Mapping

Default Filters

When the user doesn't specify filters, use these defaults (configurable in config.json):

• Experience: Entry level (code: 2)
• Remote: On-site + Hybrid (codes: 1,3)
• Date Posted: Last 24 hours (code: r86400)
• Max Pages: 2 (~50 jobs)

Filter Code Reference

Experience Levels (--experience):

• 1 = Internship
• 2 = Entry level
• 3 = Associate
• 4 = Mid-Senior level
• 5 = Director
• 6 = Executive

Job Types (--job-type):

• F = Full-time
• P = Part-time
• C = Contract
• T = Temporary
• I = Internship

Remote Options (--remote):

• 1 = On-site
• 2 = Remote
• 3 = Hybrid

Date Posted (--date-posted):

• r86400 = Last 24 hours
• r604800 = Last week
• r2592000 = Last month

Cron Setup (Optional)

Once you've configured your search profiles, you can ask the agent to set up automated monitoring:

• "Run my LinkedIn job searches every hour"
• "Check for new jobs every 30 minutes"
• "Set up daily job monitoring at 9 AM"

The agent will configure the appropriate cron schedule in OpenClaw based on your preference.