ai-job-search (Claude Code job application framework)

I came across a GitHub trending repo, MadsLorentzen/ai-job-search, and it’s one of the more engineering-forward “AI agent as a workflow” projects I’ve seen lately.

What it does: the project is a job search + application framework built to be driven through Claude Code. The core loop is straightforward: ingest job postings, have the model evaluate them against a candidate profile, then generate role-specific artifacts (CV updates and cover letters) and prep material for interviews. The repo’s pitch is essentially “fork it and own it,” which matches how I think about productionizing: start with a deterministic workflow and keep the data/control boundaries under your control.

When I’d use it: if you’re the kind of engineer who has 30–80 roles in a pipeline and you’re spending evenings doing repetitive filtering and rewriting, this kind of agent workflow can turn that into a repeatable batch process.

What to look at first when you open the repo:

- Entry point: how Claude Code is invoked and which prompt/workflow files it wires together - Data model: how job postings + your profile are represented (so you can swap sources safely) - Outputs: where it saves CV/cover letter/interview prep artifacts so you can review before sending - Safety knobs: any guardrails for mismatched roles, missing requirements, or tone/style constraints

I’m not treating it as a magic recruiter; I’m treating it as a baseline workflow you can adapt to your stack (and then add persistence via Postgres/GCP, evaluation logging, and human-in-the-loop review).