Intelligent Resume Job Matching

Job Search Problem

Job screening often breaks into separate tasks: resume content lives in PDF, Word, or image files; job postings are spread across recruitment platforms, company career pages, and startup-focused channels; and each shortlisted role still needs manual checks for company risk, listing consistency, and fit. @user_ab5ae6ee/unclecheng-job-research connects these steps into one workflow: parse the resume, search and match roles, assess company risk, and produce a structured report.

How It Works

• Resume parsing: extracts basic information, work history, project experience, skill stack, education background, and target intent, then builds a structured capability profile.
• Job search and matching: searches multiple recruitment channels, filters roles by skills, intent, company type, and hiring stage, and outputs role name, company, size, salary, location, match score, and the job posting link.
• Company due-diligence and role assessment: after the user selects a target role, it checks whether the listing is consistent, gathers public registration details, registration age, business scope, legal risks, administrative penalties, social security data, and employee reviews, then gives a 0-100 fit score, risk level, and application advice.
• Report output: generates a structured Markdown report and can export a PDF using export_pdf.py, making it easier to archive or compare candidates later.

Boundaries and Caveats

This skill is useful for campus hiring, internships, and experienced-hire screening, especially when engineers need a shortlist and pre-application assessment. It depends on public web data, resume text quality, and access to recruitment pages, so it does not replace formal corporate due diligence, legal advice, or authoritative background checks. Verify sensitive fields such as salary, litigation, penalties, and social security data against official or trusted sources before applying.