Optimizing Resumes

Resume Optimizer

Overview

Improve resume content through ATS optimization, impact quantification, keyword alignment, and content critique. Tailor resumes for specific target roles by analyzing job descriptions and recommending strategic improvements.

Quick Start

For Job-Specific Tailoring

1. Fetch or paste job description
2. Analyze requirements and identify keyword gaps
3. Tailor resume content to match job requirements
4. Apply ATS optimization and quantify impact

See: [Job Tailoring Guide](references/job-tailoring.md)

For General Optimization

1. Run content quality review on all bullets
2. Add metrics and quantifiable results
3. Strengthen action verbs and remove weak language
4. Apply ATS optimization guidelines

See: [General Optimization Guide](references/general-optimization.md)

Workflow Decision Tree

Start with resume YAML file
    ↓
Do you have a target job description?
    ↓
    ├─ YES → Job-Specific Tailoring
    │         ↓
    │         1. Analyze job posting
    │         2. Keyword gap analysis
    │         3. Tailor content (summary, bullets, skills)
    │         4. ATS optimization
    │         5. Quantify impact
    │
    └─ NO → General Optimization
              ↓
              1. Content quality review
              2. ATS optimization
              3. Quantify impact
              4. Strengthen language

Job-Specific Tailoring

1. Analyze Job Posting

Fetch from URL:

uv run scripts/fetch_job_posting.py "" --output job_description.txt

Or use pasted text: Save directly to job_description.txt

Extract:

• Required qualifications (must-have skills, experience, education)
• Preferred qualifications (nice-to-have)
• Key responsibilities and deliverables
• Priority keywords (mentioned multiple times or in title)

Detailed guide: [Job Tailoring Guide](references/job-tailoring.md)

2. Keyword Gap Analysis

Compare resume against job description:

• Critical gaps: Required qualifications you have but didn't mention
• Easy additions: Skills you have matching preferred qualifications
• Terminology mismatches: Same skill, different wording (e.g., "ML" vs "Machine Learning")
• True gaps: Required qualifications you don't have (note but don't fabricate)

Prioritize adding critical keywords to Skills section and integrating into achievement bullets.

3. Tailor Content

Professional Summary:

• Mirror language from job description
• Highlight most relevant experience
• Include target job title or similar phrasing
• Feature 2-3 top required skills

Experience Bullets:

• Reorder to prioritize most relevant achievements first
• Rewrite to incorporate job description keywords
• Emphasize experiences matching target role responsibilities

Skills Section:

• Promote required skills to top of categories
• Add missing keywords (if you have the skill)
• Group similar technologies mentioned in job

Examples: [Optimization Examples](references/examples.md)

ATS Optimization

Quick ATS Checklist

Section headers:

• [ ] Use standard headers (Experience, Education, Skills, Summary)
• [ ] Avoid creative or unusual section names

Keywords:

• [ ] Include exact phrases from job description
• [ ] List acronyms AND spelled-out versions (e.g., "Natural Language Processing (NLP)")
• [ ] Feature high-priority skills in Skills section
• [ ] Integrate keywords naturally in achievement bullets

Formatting:

• [ ] Simple, clean structure (no tables, columns, text boxes)
• [ ] Standard fonts
• [ ] Contact info at top (not in header/footer)
• [ ] Consistent date formatting
• [ ] Plain bullet points (•, -, or ◦)

Content structure:

• [ ] Reverse chronological experience
• [ ] Clear company names, job titles, dates
• [ ] Action verbs starting each bullet
• [ ] Quantified achievements

Comprehensive guide: [ATS Guidelines](references/ats_guidelines.md)

Keyword Integration Tiers

Tier 1 (required skills):

• Add to Skills section if missing
• Mention in Professional Summary
• Incorporate into 2-3 achievement bullets

Tier 2 (preferred skills):

• Add to Skills section if missing
• Mention in at least 1 achievement bullet

Tier 3 (nice-to-have):

• Add to Skills section if relevant

Quantify Impact

Impact Formula

[Action Verb] + [What You Did] + [How You Did It] + [Measurable Result]

Metric Categories

Add at least one metric from these categories to each bullet:

• Time/Speed: Reduced X from [time] to [time], improved by X%
• Cost/Revenue: Saved $X, generated $X revenue, reduced costs by X%
• Scale/Volume: Processed X items, scaled to X requests, served X users
• Quality/Accuracy: Improved accuracy from X% to Y%, reduced errors by X%
• Team/Adoption: Led team of X, adopted by X teams, onboarded X users
• Efficiency: Automated X% of process, eliminated X hours of manual work

If exact metrics unavailable:

• Estimate based on scope: "~500K users", "10M+ requests"
• Compare to baseline: "3x faster than previous approach"
• Describe scale: "across 15+ microservices"

Detailed patterns: [Impact Patterns](references/impact_patterns.md)

Action Verb Strengthening

Replace weak verbs:

• "Helped" → "Enabled", "Facilitated"
• "Worked on" → "Developed", "Built", "Implemented"
• "Responsible for" → "Owned", "Managed", "Led"
• "Participated in" → "Contributed to", "Collaborated on"

Full list: [Action Verbs](references/action_verbs.yaml)

General Content Review

Quality Checklist

For each experience bullet:

• [ ] Starts with strong action verb
• [ ] Includes what you did AND how you did it
• [ ] Contains at least one metric or quantifiable result
• [ ] Is specific (not vague)
• [ ] Is concise (1-2 lines maximum)
• [ ] Demonstrates impact (not just activities)

For Professional Summary:

• [ ] 2-4 sentences
• [ ] Highlights years of experience and seniority level
• [ ] Mentions 3-5 core competencies
• [ ] Includes industry/domain context

For Skills section:

• [ ] Organized by logical categories
• [ ] Most important skills listed first
• [ ] Specific (not "Programming" but "Python, Java, Go")
• [ ] No outdated technologies

Detailed guide: [General Optimization](references/general-optimization.md)

Common Issues

Weak bullets (activity-focused): ❌ "Worked on machine learning models" ✅ "Developed GBM churn model achieving 0.84 AUC, reducing churn by 15% and retaining $5M ARR"

Vague impact: ❌ "Improved system performance" ✅ "Reduced API response time from 800ms to 120ms through Redis caching"

Missing context: ❌ "Built data pipeline" ✅ "Built PySpark ETL pipeline processing 50M+ daily transactions with 99.9% data quality"

More examples: [Optimization Examples](references/examples.md)

Resume Length Guidelines

• Early career (0-5 years): 1 page
• Mid-career (5-10 years): 1-2 pages
• Senior/Staff (10+ years): 2 pages
• Executive: 2-3 pages

Bullet count per role:

• Current role: 4-6 bullets
• Recent roles (last 5 years): 3-5 bullets
• Older roles (5-10 years ago): 2-3 bullets
• Very old roles (10+ years ago): 1-2 bullets or consolidate

Final Optimization Checklist

Content:

• [ ] All bullets start with strong action verbs
• [ ] Every bullet includes measurable impact when possible
• [ ] Professional summary is tailored and compelling
• [ ] Skills section highlights most relevant capabilities
• [ ] No weak, vague, or passive language
• [ ] Appropriate length for experience level

ATS Optimization:

• [ ] Standard section headers throughout
• [ ] Keywords from job description integrated naturally
• [ ] Simple, clean formatting
• [ ] Contact info at top
• [ ] Consistent formatting

Target Role Alignment (if applicable):

• [ ] Keywords from job description present in resume
• [ ] Professional summary mirrors job requirements
• [ ] Most relevant experience highlighted first
• [ ] Skills section prioritizes target role requirements
• [ ] Language and terminology matches job description

Output

Provide optimized YAML resume file with:

1. Updated professional summary
2. Reordered and rewritten experience bullets
3. Enhanced skills section
4. Added metrics and impact quantification
5. ATS-optimized formatting
6. Summary of changes made and rationale

Reference Documentation

• [Job Tailoring Guide](references/job-tailoring.md) - Detailed job-specific optimization
• [General Optimization Guide](references/general-optimization.md) - Content quality and best practices
• [ATS Guidelines](references/ats_guidelines.md) - Comprehensive ATS requirements
• [Impact Patterns](references/impact_patterns.md) - Templates for quantifying achievements
• [Action Verbs](references/action_verbs.yaml) - Strong verbs categorized by impact type
• [Optimization Examples](references/examples.md) - Before/after transformations

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: vibewith-brent
• Source: vibewith-brent/claude-resume-skills
• License: MIT

Install and usage instructions live in the source repository linked above.