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resume-tailor

Tailor a resume to a specific job description by extracting target keywords, scoring keyword match, and rewriting bullets for impact and relevance. Use when applying to a specific role, optimizing for ATS keyword match, or when the user mentions resume tailoring, job application, ATS optimization, or cover letter customization.

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Atualizado há 4 meses

Ver no GitHub ↗ Licença: NOASSERTION

Como adicionar

/plugin marketplace add borghei/Claude-Skills

O comando exato pode variar conforme o repositório. Confira o README no GitHub.

Para o autor da skill

Cole no README do seu repo

Mostra que sua skill está catalogada na Skillteca, gera backlink e tráfego rastreável.

[![Skillteca](https://www.skillteca.com.br/api/badge/resume-tailor-borghei/svg)](https://www.skillteca.com.br/skills/resume-tailor-borghei)

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Ler descrição completa

Resume Tailor

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

Table of Contents

  • [Keywords](#keywords)
  • [Quick Start](#quick-start)
  • [Core Workflows](#core-workflows)
  • [Tools](#tools)
  • [Reference Guides](#reference-guides)
  • [Templates](#templates)
  • [Best Practices](#best-practices)

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

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:

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

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

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.

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

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

Templates

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

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.

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
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