What is AI Job Search
AI Job Search is an open-source AI job search framework based on Claude Code, through/setupbuilding a detailed career profile,/scrapeintelligently evaluating job match,/applylaunching a "draft-review" dual-agent workflow, and automatically generating customized LaTeX resumes and cover letters. The framework emphasizes that profile depth determines output quality, supports career path discovery, and can adapt to recruitment platforms in different regions.
Main features of AI Job Search
- Intelligent profile building: Through interactive interviews or importing an existing resume, automatically generate a structured career profile covering educational background, skills, behavioral assessments, and career goals.
- Job matching and search: Automatically scrape job information from multiple platforms and intelligently score it based on dimensions such as skills, experience, and cultural fit, then recommend the best-matching job opportunities after deduplication.
- Dual-agent application material generation: Use a "draft-review" dual-agent workflow to parse job requirements and automatically generate highly customized LaTeX resumes and cover letters that have undergone fact verification.
- Interview preparation assistance: Automatically generate a STAR behavioral interview case library based on personal real experiences, and provide structured interview response frameworks and technique guidance.
- Career path exploration: Through in-depth analysis of transferable skills and historical work patterns, intelligently recommend cross-industry or emerging career opportunities that users have not considered.
- Salary benchmarking: Supports connecting to external salary datasets for market-level analysis, providing data references for salary negotiations.
How to use AI Job Search
- Environment preparation: Install Claude Code CLI, Python 3.10+, Bun, and a LaTeX distribution (TeX Live or MiKTeX).
- Fork and clone the repository：
gh repo fork MadsLorentzen/ai-job-search --clone。 - Install search tools: Enter each job platform CLI directory (such as jobindex-search/cli) and run
bun install。 - Build a personal profile: Run
claudeto enter the CLI, execute the/setupcommand, and fill in background, skills, and career goals through an interactive interview or by importing an existing resume. - Search for jobs: Execute
/scrapeto automatically scrape jobs from multiple platforms and evaluate match. - Apply for jobs: Execute
/apply <职位链接>or paste a job description to launch the dual-agent workflow to generate a customized resume and cover letter.
Core advantages of AI Job Search
- Dual-agent quality assurance: Adopts a "draft-review" dual-agent architecture. After the drafting agent generates materials, the review agent independently researches the company background and critically evaluates it, ensuring the content is professional and authentic and preventing fabricated experience.
- Authenticity verification mechanism: All application materials must be strictly verified against the user's real profile. The system never fabricates skills or experience, ensuring the integrity and verifiability of job application materials.
- Profile depth driven: Output quality directly depends on the level of detail in the input profile. Detailed career descriptions (including specific projects, tools, and quantified results) can generate highly accurate customized content and avoid cookie-cutter templated applications.
- Career path discovery: By analyzing transferable skills and complete career history, intelligently recommends cross-industry opportunities or emerging role combinations that users have not considered, expanding job search horizons.
- Professional-grade document output: Automatically generates LaTeX-formatted resumes and cover letters, with professional and aesthetically pleasing typesetting, supports custom templates, and directly meets the requirements of formal job application scenarios.
AI Job Search project address
- GitHub repository：https://github.com/MadsLorentzen/ai-job-search
Comparison of AI Job Search with similar competitors
| Comparison dimension | AI Job Search | LoopCV | Teal |
|---|---|---|---|
| Product positioning | Open-source intelligent job search framework | Fully automated batch application platform | Job tracking and optimization workspace |
| Technical architecture | Local framework based on Claude Code CLI | SaaS cloud platform | SaaS cloud platform |
| Degree of automation | Semi-automatic (requires executing /apply command to trigger) |
Fully automatic (7×24-hour continuous background applications) | Manual (single-document optimization assistance) |
| Customization capability | Extremely high (LaTeX source-level customization, dual-agent narrative restructuring) | Medium (automatic field filling based on profile) | Medium-high (modular editing suggestions) |
| Authenticity assurance | Dual-agent verification(review agent ensures zero fabrication) | Depends on the accuracy of the user's preset profile | User ensures it themselves, AI provides optimization suggestions |
| Technical threshold | High(requires configuring Python, Bun, and LaTeX environments) | Low(pure web operation) | Low(browser extension + web page) |
Application scenarios of AI Job Search
- Large-scale precise submissions: Suitable for job seekers who need to apply to a large number of positions but refuse templated applications, by automatically generating deeply customized LaTeX resumes and cover letters for each company, improving submission efficiency while maintaining high quality.
- Cross-industry/career change job search: Suitable for users who want to change career tracks or enter emerging fields, using the career path discovery feature to identify transferable skills and reframe past experience into relevant qualifications for the new field.
- Sorting out complex backgrounds: Suitable for candidates with diverse career experiences and rich project types, through structured profile building, integrating scattered skills and achievements into a logically clear career narrative, avoiding disorganized application materials.
- Systematic interview preparation: Aimed at scenarios requiring handling behavioral interviews, automatically generating a STAR case library based on real experiences, providing structured response frameworks and in-depth company research support.
- Job search in professional fields: Suitable for scenarios such as academia, scientific research, and high-end technical positions that require precise expression of professional abilities, using LaTeX to generate professional documents that meet industry standards, ensuring the accuracy of technical terminology and project descriptions.