JD Resume Matcher

Problem

In hiring, job descriptions and resumes often use different vocabularies. JDs may be vague, mix outdated or overly narrow skill requirements, and hide the real responsibility focus. Resumes may list projects and tools that are hard to map to the role. Manual matching can overreact to keywords, while checklist-only comparison misses context.

How It Works

This skill is built for HR workflows and technical recruiters around three tasks:
- JD analysis: extracts basic information, responsibilities, and requirements, then reports JD quality, core issues, missing dimensions, optimization suggestions, and hiring risks.
- resume-JD matching: compares the resume against the JD using role profiles, covering match overview, skill comparison, bonus signals, strengths, gaps, and interview suggestions.
- skill-market validation: combines a static skill taxonomy with AI semantic reasoning to check whether the required skills are market-reasonable; unknown skills can be supplemented via web search with reduced confidence.
It reads reference files only for the active mode: mode 0 uses skill taxonomy and job profiles, mode 1 uses output format, and mode 2 uses output format plus job profiles. Scoring formulas and JD parsing logic are implemented in scripts, and results are rendered to stdout by output_formatter.py, then shown directly in chat without creating .md, .docx, or .xlsx files.

Boundaries

This is useful for JD review, initial resume screening, and interview preparation. It does not replace hiring decisions or human judgment. When a skill or role is not in the local knowledge base, the conclusion depends on web search and should be interpreted with the reported confidence level.