Resumeet: An Intelligent Resume Analysis and Job Matching System Based on Gemini 2.5 Flash

Resumeet is an open-source project that uses the Google Gemini 2.5 Flash model to achieve deep contextual resume parsing, skill gap identification, and personalized improvement suggestions. It addresses issues like low efficiency and inaccurate matching in traditional recruitment screening, providing AI-driven intelligent analysis capabilities for both recruiters and job seekers.

In traditional recruitment, HR spends a lot of time screening massive resumes, making it hard to quickly identify candidates' true abilities and job matching degrees. Job seekers often struggle with resumes failing ATS screenings or not knowing how to optimize them. AI technology offers new possibilities to solve these problems. As an intelligent tool based on large language models, Resumeet provides accurate matching analysis and improvement suggestions through deep contextual understanding rather than simple keyword matching.

Core Components: Google Gemini 2.5 Flash is selected as the intelligent engine (high reasoning ability, fast response, cost-effective); the backend uses Python/Flask to build a lightweight web service; Google GenAI SDK is used to interact with the model; PyPDF2 handles PDF file extraction; the frontend is an HTML5/CSS3 responsive interface. Workflow: 1. Resume parsing and information extraction (PyPDF2 processes PDFs to ensure complete information from complex layouts); 2. Intelligent analysis and matching (submit resume text + job description to the Gemini model to identify core skills/experiences, evaluate matching degree, recognize skill gaps, and generate matching scores); 3. Result presentation and suggestions (visually display matching scores, skill comparisons, gap analysis, and improvement suggestions).

Matching Score Mechanism: Combines skill matching degree (tech stack overlap + deep relevance), experience relevance (project/work experience correlation with the job), and potential assessment (learning and growth potential of junior/transitioning candidates). Skill Gap Analysis: Technical skill gaps (skills required by the job but not reflected in the resume, sorted by importance); soft skill gaps (inferred from experience descriptions, such as communication/leadership). Improvement Suggestions: Operability (specific examples like "Add Python data analysis project experience"); priority ranking (high ROI items first); job-specificity (customized guidance).

Job Seekers: ATS optimization guidance (adjust format and keywords to improve pass rate); job fit analysis (highlight relevant experiences and skills); career development suggestions (clarify learning priorities). Recruiters: Bulk resume initial screening (quickly process large volumes of resumes); standardized evaluation (reduce subjective bias); interview preparation guidance (design targeted questions).

Planned developments: AI interview question generator (generate customized questions based on skill gaps); exportable PDF reports (for candidates to reference or attach to applications); multi-language support (non-English resumes/JDs); integration expansion (API interfaces/browser plugins to integrate with platforms like LinkedIn).

Resumeet combines large language model semantic understanding with recruitment expertise to provide an intelligent resume analysis and matching solution. Its success lies in focusing on core pain points, selecting appropriate technologies, maintaining a concise architecture, and emphasizing user experience. In the future, with the improvement of large model capabilities and data accumulation, it will play a more important role in talent matching, realizing the vision of "connecting the right people with the right opportunities."