Linkedin Job Search

linkedin-job-search

Searches LinkedIn for jobs matching a keyword + location, filters results against your profile, and saves a shortlist.

Description

Actively searches LinkedIn job listings (not just your saved ones) using a Playwright browser with your existing session. Scrapes candidates across pages, fetches full job descriptions, scores them against your profile, and outputs a filtered shortlist — ready to hand off to /job-analyzer or /resume-cover-letter-generator.

Usage

/linkedin-job-search
Keywords: Supply Chain Manager
Location: Oslo, Norway
Save top: 10
Flags: flag if Norwegian required, note if relocation expected

Or minimal:

/linkedin-job-search
Keywords: Operations Manager
Location: London, UK

Inputs

If no resume is provided in the message, ask the user to share it (text paste or file path). The resume is the source of truth for preferences — do not invent a profile.

Workflow

Phase 1 — Setup & Scrape with Descriptions

1. Copy linkedin_search.mjs to /tmp/pw-runner/ if not already there:

   mkdir -p /tmp/pw-runner
   cp /path/to/repo/scraper/linkedin_search.mjs /tmp/pw-runner/
   cd /tmp/pw-runner && npm install playwright 2>/dev/null | tail -2
   

2. Run the scraper — the browser opens, sets the location filter, then clicks through every card, waits for the right panel to load, and reads the full job description. Use --max-jobs to cap how many jobs are scanned (stops mid-page when reached):

   cd /tmp/pw-runner && node linkedin_search.mjs \
     --keywords "Supply Chain Manager" \
     --location "Oslo, Norway" \
     --max-jobs 50 2>&1
   

3. Parse the JSON block after === SEARCH RESULTS === — each entry has title, company, location, url, easyApply, posted, and description (up to 3000 chars of the full JD).

Session handling: Same session as linkedin-jobs-fetch (~/.claude/linkedin-session.json). If the script reports session expired, tell the user to delete that file and re-run. If no session exists, the browser will open for manual login — instruct user to log in then run touch /tmp/linkedin-ready.

Phase 2 — Evaluate

1. Extract user profile from provided resume: current role, skills, seniority, domain background, languages, location preference.
2. Note any custom flags from user message (language requirements, visa, remote vs on-site, etc.).
3. For each scraped job, assess using the description field (no separate WebFetch needed):
   • Domain fit — honest about skill gaps
   • Level fit — seniority match
   • Location fit — matches user preference?
   • Custom flags — apply ❌/⚠️/✅ markers
   • Overall fit — ⭐ to ⭐⭐⭐⭐⭐
4. Sort by overall fit descending, pick top N URLs.

Phase 3 — Save on LinkedIn

Run the scraper in save mode — it navigates the same search page, clicks through cards, and clicks the LinkedIn "Save" button on each matching job:

cd /tmp/pw-runner && node linkedin_search.mjs \
  --keywords "Supply Chain Manager" \
  --location "Oslo, Norway" \
  --save-urls "https://www.linkedin.com/jobs/view/123/,https://www.linkedin.com/jobs/view/456/"

Parse === SAVE RESULTS === to confirm how many were saved.

Phase 4 — Output

Present shortlist as a markdown table:

Save full shortlist to:

output/job-search-YYYY-MM-DD.json

After outputting the table, offer:

• /job-analyzer — for deeper per-job analysis with full summaries
• /resume-cover-letter-generator — to generate tailored PDFs for top picks

Key Rules

• Always read actual JD — never assess a job from title alone; descriptions come from the scraper's description field, no WebFetch needed
• Always read actual resume — ask if not provided, never invent a profile
• Be honest about gaps — don't oversell fit
• Never unsave — save mode skips jobs already saved (aria-label="Unsave the job")
• Create output/ folder if it doesn't exist before saving JSON
• Idempotent — re-running overwrites the same-date output file cleanly

Related Skills

• linkedin-jobs-fetch — fetches jobs you've already saved on LinkedIn
• job-analyzer — deeper analysis of a job list
• resume-cover-letter-generator — generates tailored PDFs for top picks