AI Framework for Automating Job Hunting with Claude Code Now Available
The open-source AI job search framework "ai-job-search" is gaining attention on GitHub. Based on Claude Code, it automates everything from job searches to application preparation and interview readiness. Targeted at the Danish market but designed for general use.
An open-source framework called “ai-job-search” is gaining traction on GitHub for its ability to delegate every aspect of the job-seeking process to an AI agent—from job search and resume and cover letter creation to interview preparation. Built on Anthropic’s CLI tool “Claude Code,” this project aims to automate the job application process much like developers automate coding tasks.
This project is an independent open-source initiative and is not an official product of Anthropic or Claude Code. Developed by Mads Lorentzen, the framework is designed to execute a comprehensive workflow on Claude Code, including self-profiling, aptitude assessment, drafting application documents, and providing critique and revisions.
Overview of the Workflow
The core of ai-job-search consists of three primary commands:
The /setup command builds the user’s profile. It offers three pathways: setting up existing CV PDFs, LinkedIn export data, diplomas, and recommendation letters in a document folder; directly pasting a CV in a chat; or registering information through an interview format with the AI. The profile is saved in a structured format for reuse in subsequent application processes.
The /scrape command gathers job postings from job portals. Currently, it supports major Danish job sites like Jobindex, Jobnet, Akademikernes Jobbank, Jobdanmark, and LinkedIn. However, the framework’s design allows for the replacement of job boards with country- or region-specific alternatives, enabling developers to create and integrate their custom local job search CLI tools.
The /apply command is the framework’s most powerful feature. Based on the collected job postings and the user’s profile, the AI evaluates eligibility, assigns scores, and suggests suitable positions. Once the user selects a position, the AI drafts a resume (CV) and cover letter using LaTeX. A two-stage pipeline is then employed, where a reviewer agent critiques the drafts, incorporates revisions, and generates the final output.
Technical Stack and ATS Compatibility
The technical stack for ai-job-search includes Claude Code (CLI), Python 3.10 or later, Bun (runtime for the job scraping CLI tool), and a LaTeX distribution (TeX Live or MiKTeX). It uses lualatex for compiling resumes and xelatex for cover letters.
Notably, the framework also supports ATS (Applicant Tracking System) compatibility. During the /apply workflow, the generated CV is subjected to an ATS parsability check using pdftotext (part of the poppler package). If the tool is not installed, the process falls back to a visual keyword review mechanism.
Integration of Career Guidance
This framework is more than just a document creation tool. It incorporates best practices in career guidance, offering structured evaluation criteria, forward-thinking framing for cover letters, and an optional salary benchmarking feature. Users can analyze salary ranges based on industry standards and job roles at the target companies.
Developer Mads Lorentzen aims to reduce the cognitive load associated with job hunting while improving the quality of applications. Though tailored for the Danish market, the core workflow—self-profiling, aptitude evaluation, and the drafter-reviewer pipeline—is designed to be language- and country-agnostic.
Editorial Opinion
The ai-job-search framework is an excellent example of how AI agents can complete specific workflows (such as job hunting) on behalf of humans. In the short term, it has the potential to significantly enhance the productivity of job seekers, especially engineers who often spend substantial time drafting applications. While it currently focuses on the Danish job market, the community could adapt it for Japanese job services, allowing users in Japan to reap similar benefits.
From a long-term perspective, the widespread adoption of such tools could lead to significant changes in the roles of recruitment agencies and career advisors. As AI standardizes the quality of application documents and optimizes them for initial screenings, a new form of competition may emerge. Concurrently, hiring companies will face the challenge of assessing AI-generated applications.
As an editorial team, we are keen to observe how human qualities and individuality will be valued in a labor market increasingly mediated by AI, as well as how this project evolves and adapts to the Japanese market.
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