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Resume Tailor

Tailor an evidence-grounded resume to a specific job by reordering and rewriting true experience, without arbitrary match scores or keyword-density targets.

中文简介正在准备中。当前显示原文说明。

splash0047/resume-generator星标 34(GitHub 34 + cccskills 0)

实测评分 未实测

查看安装方法

包含的文件(1)

  • SKILL.md2.1 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Resume Tailor

Inputs

  • target job description;
  • eligibility + requirement analysis;
  • candidate master profile;
  • verified project/experience evidence;
  • selected role-family resume.

Tailoring Rules

1. Eligibility First

Do not hide a hard requirement mismatch with wording.

2. Reorder Before Rewriting

First:

  • choose the correct role variant;
  • lead with the most relevant experience/project;
  • reorder skills;
  • remove low-relevance content.

3. Rewrite Only With Evidence

Use job terminology where it accurately describes existing work.

4. Required Criteria

For each required criterion:

  • direct evidence -> make visible;
  • indirect evidence -> represent accurately;
  • missing -> do not fabricate.

5. Preferred Criteria

Use when relevant and supported, but do not crowd out required evidence.

6. Metrics

Use only measured or reproducible metrics with provenance. Otherwise use concrete implementation detail.

7. Skills

Keep skills the candidate can explain. Do not add a tool merely because it appears in the JD.

Tailoring Output

Requirement-to-Resume Map

RequirementEvidenceResume locationAction
REST APIsJob Portal + RCAProjectsLead with concrete endpoints / auth
AWSTraining credential onlyCertificationsKeep credential; do not imply production use

Change Log

  • moved project X above project Y;
  • removed irrelevant skills;
  • rewrote bullet with verified terminology;
  • removed unsupported metric;
  • made expected graduation explicit.

Final Validation

  • all required claims truthful;
  • no forced keyword repetition;
  • one role identity;
  • links consistent;
  • exported PDF still parses correctly.

Guardrails

  • No estimated new match score.
  • No keyword-density objective.
  • No unsupported technology injection.
  • No rewriting that changes ownership, scope, environment, or measurement context.

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