Resume Match#

Use this skill when the user wants to compare their resume(s) against a specific job description and get a quantified match score, gap analysis, and tailored rewrite suggestions.

Good triggers#

• "Check if my resume fits this job posting."
• "Score my resume against this JD."
• "What's missing from my resume for this role?"
• "Optimize my resume for this job application."
• "A/B test two versions of my resume against the same JD."

Workflow#

1. Extract resume structure. Parse the resume input into:

   • Education (degrees, institutions, graduation year)
   • Work experience (companies, titles, dates, key achievements)
   • Skills (technical and soft, explicitly listed or embedded)
   • Projects or publications (if any)
   • Certifications / languages / awards

2. Extract JD requirements. Parse the job description into:

   • Must-have — explicit requirements ("5+ years Python", "Bachelor's required")
   • Nice-to-have — preferred qualifications ("experience with Kubernetes is a plus")
   • Soft skills — inferred or stated ("team player", "strong communication")
   • Hidden signals — industry keywords, hard-to-find experience the JD emphasizes

3. Compute match score (0-100). Break down by dimension:

   • Technical skills match (weighted by must-have vs nice-to-have)
   • Experience level match
   • Education/certification match
   • Soft skills evidence
   • Overall keyword density in resume vs JD

4. Gap analysis. For each JD requirement the resume does not satisfy:

   • Label GAP, PARTIAL, or MATCH
   • Suggest: upskill, rephrase, or add hidden experience
   • Estimate impact on scoring if fixed

5. 2×2 skills matrix. Plot: JD RequiresJD Doesn't RequireResume HasStrength zoneOverqualified zoneResume MissingGap zoneIrrelevant zone

6. Prioritize changes. P0 → P1 → P2:

   • P0: Must-fix gaps blocking interview (missing JD critical skill)
   • P1: Strengthen weak areas (rephrase to match JD language)
   • P2: Nice-to-have additions (low effort, moderate impact)

7. Resume rewrite. For each section, rewrite the resume to match JD language without fabricating facts:

   • Replace generic verbs with JD-aligned action words
   • Reorder bullet points to surface most relevant achievements first
   • Add missing keywords naturally (if true)
   • Adjust summary/objective to mirror JD tone

8. Keyword density check. Extract top 20 TF-IDF keywords from JD and count occurrences in original vs optimized resume. Flag density < 30% of JD frequency.

9. Deliver match report. Structured output:

   • Overall score and dimension breakdown
   • Gap analysis table
   • 2×2 matrix
   • Prioritized change list
   • Original vs optimized resume (side by side)
   • Keyword density comparison

Sample prompt#

text

resume-match match --resume resume.pdf --jd "https://example.com/job/123"