resume-ats-optimizer

これはレビュー時に保存された抜粋です。完全で最新の内容は外部ソースを確認してください。

---
name: resume-ats-optimizer
description: Optimize resumes for Applicant Tracking Systems, check ATS compatibility, and analyze keyword match
---

# Resume ATS Optimizer

## When to Use This Skill

Use this skill when the user wants to:
- Optimize their resume for Applicant Tracking Systems (ATS)
- Check if their resume will pass automated screening
- Understand why their applications aren't getting responses
- Mentions keywords like: "ATS", "not getting interviews", "resume not working", "optimize resume", "keyword optimization"

Also use when the user provides a resume file and mentions they're applying to jobs.

## Core Capabilities

- Parse resume and test ATS compatibility
- Extract and analyze keywords against job descriptions
- Identify formatting issues that break ATS parsers
- Calculate match scores between resume and job postings
- Suggest keyword additions and placements
- Generate ATS-friendly formatting recommendations

## The ATS Problem

75% of resumes are rejected by Applicant Tracking Systems before a human ever sees them. Companies use ATS to:
- Filter out unqualified candidates automatically
- Search for specific keywords from job requirements
- Parse resumes into structured data
- Rank candidates by keyword match percentage

Common reasons resumes fail ATS:
1. Poor formatting (tables, columns, headers/footers)
2. Missing keywords from job description
3. Inconsistent section headers
4. Non-standard fonts or special characters
5. Text embedded in images
6. Incorrect file format

## ATS Compatibility Checklist

### File Format
- ✅ Use .docx or .pdf (not .pages, .odt)
- ✅ PDF must be text-based, not scanned image
- ✅ File name: "FirstName_LastName_Resume.pdf"

### Font & Formatting
- ✅ Standard fonts: Arial, Calibri, Georgia, Times New Roman
- ✅ Font size: 10-12pt for body, 14-16pt for headers
- ✅ No text boxes, tables, or columns
- ✅ No headers/footers (put contact info in body)
- ✅ No images, graphics, or charts
- ✅ Consistent date formats (MM/YYYY)
- ✅ Standard bullet points (•, -, *)

### Section Headers
Use standard, recognizable headers:
- ✅ "Professional Experience" or "Work Experience" (not "Where I've Been")
- ✅ "Education" (not "Academic Background")
- ✅ "Skills" (not "Core Competencies")
- ✅ "Summary" or "Professional Summary"

### Contact Information
```
John Smith
[email protected] | (555) 123-4567 | LinkedIn: linkedin.com/in/johnsmith
San Francisco, CA
```

NOT in header/footer, and avoid:
- ❌ Tables for contact info
- ❌ Special characters in email
- ❌ Multiple phone numbers
- ❌ Full mailing address (city/state is enough)

## Keyword Optimization Process

### Step 1: Extract Job Description Keywords

Identify three types of keywords:

**Hard Skills (Technical)**
- Programming languages (Python, Java, SQL)
- Tools and platforms (Salesforce, AWS, Excel)
- Certifications (PMP, CPA, CFA)
- Methodologies (Agile, Six Sigma, SDLC)

**Soft Skills**
- Leadership, collaboration, communication
- Problem-solving, analytical thinking
- Project management, stakeholder management

**Industry Terms**
- B2B, SaaS, e-commerce
- Enterprise, SMB, mid-market
- ARR, MRR, churn rate

### Step 2: Match Analysis

For each keyword in job description:
1. Check if exact phrase appears in resume
2. Check for synonyms or variations
3. Count frequency of mention
4. Note location (summary, experience, skills)

### Step 3: Calculate Match Score

```
Match Score = (Keywords Matched / Total Required Keywords) × 100

Example:
Job has 20 required keywords
Your resume has 15 of them
Match Score = 75%

Target: 80%+ for strong match
```

### Step 4: Keyword Placement Strategy

**Priority 1: Professional Summary (Top of Resume)**
- Include 5-8 most important keywords
- Use naturally in 3-4 sentence paragraph
- Example: "Data Scientist with 5+ years using Python, SQL, and machine learning to drive business insights..."

**Priority 2: Skills Section**
- List keywords explicitly
- Group by category if needed
- Use exact phrasing from job