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name: resume-tailor description: > 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 Clause metadata: 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

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

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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, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• 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

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

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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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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