resume-tailor

name: resume-tailordescription: >  Tailor a resume to a job description by extracting keywords, scoring match,  and rewriting bullets for impact. Use when applying to a role, optimizing  for ATS keyword match, or customizing a cover letter.license: MIT + Commons Clausemetadata:   version: 1.0.0  author: borghei  category: personal-productivity  domain: career  updated: 2026-05-04  python-tools: resume_matcher.py  tech-stack: ATS, job-search  tags:     - resume    - job-search    - ats    - keywords    - career

Resume Tailor

Tailor a base resume to a specific job description with keyword-match scoring, gap analysis, and rewritten-bullet suggestions.

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Table of Contents

• Keywords
• Quick Start
• Core Workflows
• Tools
• Reference Guides
• Templates
• Best Practices
• Scope & Limitations

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Keywords

resume, CV, job application, ATS, applicant tracking system, keyword match, resume tailoring, cover letter, job description, hiring, recruiter, career, job search, bullet rewrite, accomplishment, impact statement

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Clarify First

Before tailoring the resume, settle the inputs below. When one is missing or vague and would change the result, ask about it; when it wouldn't, use a sensible default and say which one you used.

• Target job description: the verbatim JD; it drives keyword extraction and the entire match score.
• Base resume content: your actual experience and bullets; without it there is nothing to score or rewrite.
• Target seniority / title: IC vs lead vs exec sets which keywords matter and the altitude of rewritten bullets.
• Truth boundary: which listed skills you genuinely have vs. aspirational, so additions are honest rather than keyword-stuffed.

Ask about the two or three inputs that matter most for this request, not the whole list. If the user wants a result straight away, produce it and state your assumptions at the top.

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Quick Start

Tailor a Resume in 5 Minutes

1. Save the job description as jd.txt
2. Save your base resume text as resume.txt
3. Run the matcher:

   python scripts/resume_matcher.py resume.txt jd.txt
   

4. Review the keyword-gap report
5. Rewrite low-scoring bullets using references/bullet_rewrite_patterns.md
6. Cross-check the final resume against the rewritten template in assets/tailored_resume_template.md

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Core Workflows

Workflow 1: Match Score and Keyword Gap

Goal: Get a quantitative score for how well the current resume matches a target job description before submitting.

Steps:

1. Capture the job description verbatim into jd.txt
2. Run: python scripts/resume_matcher.py resume.txt jd.txt
3. Review the score — anything below 70% means significant gaps
4. Read the missing-keywords list; classify each as (a) skills you have but did not list, (b) skills you do not have, (c) buzzwords that do not apply
5. Add (a) to the resume; ignore (c); be honest about (b)

Expected Output: A score, a kept-keyword list, and a missing-keyword list.

Time Estimate: 5-10 minutes per job description.

Workflow 2: Bullet Rewrite for Impact

Goal: Convert task-oriented bullets ("responsible for…") into impact bullets that match recruiter and ATS expectations.

Steps:

1. Identify weak bullets — anything starting with "Responsible for" or "Helped with"
2. Apply the CAR pattern (Challenge, Action, Result) from references/bullet_rewrite_patterns.md
3. Quantify wherever possible (percentages, dollar amounts, time saved, scale)
4. Re-run the matcher to confirm score improvement

Expected Output: Bullet list rewritten in CAR format with metrics.

Time Estimate: 5 minutes per bullet.

Workflow 3: Cover Letter Hooks

Goal: Pull the strongest 3-5 hooks from the resume that map directly to the top requirements in the job description.

Steps:

1. Run matcher in JSON mode: python scripts/resume_matcher.py resume.txt jd.txt --json
2. Take the top 5 matched keywords by relevance
3. For each, find the matching resume bullet
4. Use them as evidence sentences in the cover letter

Expected Output: 3-5 evidence sentences for the cover letter.

Time Estimate: 10 minutes.

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Tools

resume_matcher.py

Reads a resume text file and a job description text file, returns:

• A match score (0-100) based on keyword overlap weighted by JD frequency
• A kept keywords list (in both resume and JD)
• A missing keywords list (in JD only)
• A resume-only keywords list (in resume but not JD — candidate to drop)

# Human-readable
python scripts/resume_matcher.py resume.txt jd.txt

# JSON for programmatic use
python scripts/resume_matcher.py resume.txt jd.txt --json

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Reference Guides

• references/bullet_rewrite_patterns.md — CAR pattern, action-verb library, quantification examples, weak-phrase blacklist
• references/ats_optimization_guide.md — How ATS parses resumes, formatting do's and don'ts, keyword density bounds

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Templates

• assets/tailored_resume_template.md — A bare resume skeleton with section ordering, length guidance, and keyword-placement notes. Fill in your content.

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Best Practices

• Tailor every time. A generic resume sent to ten roles performs worse than ten tailored versions.
• Honesty over keyword stuffing. Add only skills you actually have. Hiring managers can tell.
• Keep one master resume. Tailor variants from a single source of truth.
• Two pages max. Even for senior roles, two pages is the ceiling outside academia.
• Plain text, single-column. ATS systems still mishandle tables, graphics, and multi-column layouts.

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Scope & Limitations

In Scope:

• Keyword match scoring of a resume against a job description, weighted by JD frequency, with kept, missing and resume-only keyword lists (resume_matcher.py).
• Bullet rewrites with the CAR pattern, an action-verb library and a weak-phrase list in references/bullet_rewrite_patterns.md.
• ATS formatting guidance in references/ats_optimization_guide.md and a resume skeleton in assets/tailored_resume_template.md.
• Cover letter evidence sentences drawn from matched keywords.

Out of Scope:

• Parsing PDF or DOCX resumes. Both inputs are plain text files.
• Interview preparation (see project-management/career/pm-interview-prep/ for product management roles).
• LinkedIn profile work (see tools/linkedin/linkedin-profile-optimizer/).
• Hiring-side screening and job description writing (see hr-operations/talent-acquisition/).

Important Caveats:

• The match score is keyword overlap. It does not reproduce any specific ATS, and real systems parse and weight fields differently.
• Synonyms and different phrasing count as misses, so the missing-keyword list needs human review.
• A higher score is not a better resume. Adding skills the candidate does not have raises the score and misrepresents the candidate.

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Integration Points

• Pairs with personal-productivity/lead-researcher/ for prepping informational interviews
• Pairs with marketing/copywriting/ for cover letter prose quality
• Used by agents/personas/ workflows when authoring sample profiles