Smart Resume Job Matching Assistant

Problem

Job hunting often gets stuck in formatting resumes, repeatedly searching job boards, and verifying company risk. Candidates usually have to parse their own CV, refresh multiple platforms, and separately check business registration, legal cases, and employee reviews. This skill puts resume parsing, job matching, and company due diligence into one workflow.

How It Works

• Resume parsing: Supports PDF, Word, and JPG/PNG files, extracting basic information, work experience, projects, skill stack, education, and job preference into a structured profile.
• Job search and matching: Searches public channels such as Zhaopin, 51Job, BOSS Zhipin, Liepin, Lagou, and company career pages. It ranks the Top 10 roles by skill and preference, and labels company size, salary range, location, fit score, and application link.
• Company due diligence: Cross-checks job postings, collects registered capital, business scope, litigation, debt disputes, administrative penalties, and social-insurance data where available, then compares the role requirements against the resume profile with a 0-100 fit score and application advice.
• Report output: Produces a Markdown assessment report and can export PDF via export_pdf.py for saving and review.

Boundaries And Notes

• Job data comes from live web search and public job boards; salary and availability can change, so verify the official posting before applying.
• Company checks rely on public business, legal, and review information. They are useful for initial screening but are not a substitute for formal legal or financial due diligence.
• Fit scores compare the resume profile with the job description. Treat them as ranking signals, not hiring decisions.
• Image resumes, scans, and heavily formatted documents may reduce parsing quality; use a clean PDF or Word version when possible.