From Laid Off to AI Engineer: Building a Job Search Agent with Claude

⚡ TL;DR: Mads built an open-source AI agent using Claude that scrapes job boards, scores job fit, and drafts tailored resumes. By using a 'human-in-the-loop' approach, he automated the boring parts while keeping the final decision-making power.

The Simple Version

Searching for a job usually feels like a full-time job itself. You spend hours scrolling, tweaking your resume for every single application, and hoping someone notices.

Mads's system acts like a world-class career coach and a personal assistant rolled into one. It does the hunting and the drafting, so you only have to do the reviewing and the applying.

How It Actually Works

1. Automated Discovery

The process starts with a LinkedIn Scraper. This tool automatically finds job listings that match a specific profile, removing the need for manual searching.

2. The Fit Analysis

Not every job is a good match. The AI analyzes the job description against the user's profile and assigns a fit score.

3. Tailored Content Generation

Once a job is deemed a 'fit,' Claude takes over. It generates a customized resume and cover letter specifically for that role's requirements.

4. The Reviewer Agent

To ensure quality, Mads uses a multi-agent system. One AI agent writes the application, and a second 'Reviewer' agent critiques it for errors or lack of impact.

Technical Deep Dive

From a developer's perspective, this isn't just a prompt; it's an orchestrated workflow. The system leverages multi-agent orchestration to separate the concerns of generation and validation.

By splitting the 'Writer' and 'Reviewer' into separate prompts or agent personas, the system reduces hallucinations and increases the precision of the tailoring process. This is a classic implementation of the Critic-Actor pattern in AI architecture.

Furthermore, Mads treats these workflows as reusable skills on GitHub. Instead of sharing a static tutorial, he provides the actual logic and prompts, allowing other developers to fork and adapt the workflow to their own professional needs.

Why This Matters

This project highlights a massive shift in how we use LLMs. We are moving away from simple 'chatting' and toward autonomous agents that can execute complex, multi-step business processes.

It also proves the value of the 'Human-in-the-Loop' philosophy. By intentionally avoiding 'one-click auto-apply,' Mads ensures the quality remains high and the human connection is preserved.

Finally, the 30K GitHub stars show a growing hunger for practical AI implementation. People don't just want to know what AI can do; they want the actual blueprints to automate their lives.

Key Takeaways

• Automation over Manual Labor: AI can handle the repetitive 'grunt work' of job hunting.
• Multi-Agent Quality: Using a 'Reviewer' agent significantly improves the output of a 'Writer' agent.
• Human-in-the-Loop: Automation is best used to prepare the work, while humans make the final decision.
• Fit Scoring: Use AI to quantify opportunity quality before spending time on an application.
• Workflows as Code: Reusable AI workflows on GitHub are becoming the new standard for sharing knowledge.
• Tailoring is King: Hyper-personalized resumes are far more effective than generic ones.