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How to rank a batch of scraped jobs into a shortlist with AI Job Search

Scrape job portals, then score every new posting against your fit framework in one run.

Who it is forA job seeker whose search returned dozens of postings and who does not want to read each one by hand.

The problemScrape results are often too many to evaluate one by one, and weak matches waste time.

The outcomeA ranked shortlist with strengths, gaps, deadline flags and expired postings marked, where you can pick a number to start /apply.

What you need first

  • Claude Code (Pro, Max, Team subscription or Anthropic API credits)
  • Python 3.10+
  • Bun
  • A LaTeX distribution with lualatex and xelatex
  • git and the gh CLI to fork the repo
  • Your CV, past applications and other career documents to load into /setup

Steps

  1. gh repo fork MadsLorentzen/ai-job-search --clone
  2. python3 tools/install_portal_clis.py
  3. Run claude inside the repo, then run /setup to build your profile.
  4. Run /scrape to search the job portals and deduplicate results.
  5. Run /rank to score the new postings and review the ranked shortlist before picking one to apply to.

Prompt for your agent

In the ai-job-search repo, run /scrape and then /rank. Expect a ranked shortlist with per-job strengths and gaps, with deal-breakers applied as vetoes. Verify that the top-ranked job matches your stated deal-breakers and salary expectations.

Caveat. The job portal search skills are built for the Danish market (Jobindex, Jobnet, Akademikernes Jobbank), so results outside Denmark depend on adapting the portal skills.

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