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Score your resume against any job description — keyword gap analysis in seconds
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Browse Productivity KitsMost ATS systems reject resumes before a human ever reads them — not because the candidate is unqualified, but because the resume lacks the exact keywords the recruiter used in the job posting. This tool runs the same token-matching logic to show exactly which keywords are missing from your resume and what your ATS match score would be.
The tool extracts keywords from the job description by stripping stop words and identifying significant single-word terms, two-word phrases, and three-word technical phrases. Each keyword is weighted by frequency and specificity — a phrase like "React Native" counts more than a common word like "experience".
The resume text is then scanned for each keyword using case-insensitive whole-word matching. The score is calculated as the ratio of matched keywords to total job description keywords. Formatting warnings check for known ATS failure patterns: special characters in bullet points, unusual section headers, and table-based layouts.
A software engineer updating their resume before applying to a specific role and needing to know exactly which skills to add.
A career switcher who has the right experience but uses industry-specific terminology from their old field instead of the target role.
A recruiter stress-testing a job description to check whether it accidentally filters out qualified candidates through overly specific keyword requirements.
Paste your full resume text into the left panel
Paste the job description you are applying to into the right panel
Click "Check ATS Score" to run the analysis
Review your score, missing keywords, and formatting tips
About the ATS Resume Checker
The score is the percentage of significant keywords from the job description that also appear in your resume. A score above 70% means strong keyword overlap. Below 50% means an ATS system would likely filter out your resume before a human sees it.
No. The analysis is pure text matching — stop word filtering, keyword extraction, and phrase detection. This mirrors what most commercial ATS systems actually do: they tokenize text and match keywords, not interpret meaning.
Single words longer than 3 characters (excluding common stop words like "the", "and", "for") plus two-word and three-word noun phrases found in the job description. Technical terms, job titles, tools, and skills get the highest weight.
Yes, significantly. Many ATS systems parse PDFs as plain text and cannot read multi-column layouts, tables, or text boxes. Formatting warnings in this tool flag exactly these issues. When in doubt, use a single-column text resume.
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