- Keyword coverage from the job description.
- Section headings for fast scanning.
- Length balance for typical screening.
- Formatting risk markers like tables and columns.
- Readability using a Flesch-based estimate.
Example Data Table
| Scenario | Inputs (summary) | Outputs (example) |
|---|---|---|
| Data Analyst role | Resume mentions SQL, dashboards, and Python. Job asks SQL, BI tools, and stakeholder reporting. | Overall 82/100, Keyword 78, Formatting 92, Missing: "Tableau", "A/B testing". |
| Marketing Specialist role | Resume highlights campaigns and SEO. Job emphasizes analytics, paid ads, and reporting cadence. | Overall 74/100, Keyword 70, Sections 83, Missing: "ROAS", "Google Ads". |
| Junior Developer role | Resume is short and lacks headings. Job requires Git, testing, and API basics. | Overall 61/100, Length 55, Sections 50, Missing: "unit tests", "REST API". |
Formula Used
The checker produces five component scores and combines them into one compatibility score. Scores are capped between 0 and 100 to keep interpretation consistent.
- Keyword Match: percentage of extracted job terms present in your resume.
- Formatting: penalties for table/column markers and heavy separators.
- Readability: a Flesch-based estimate mapped to a 0–100 score.
- Section Coverage: detected headings like Skills, Experience, Education.
- Length: word count compared to an ATS-friendly range.
How to Use This Calculator
- Paste your resume into the Resume text box.
- Paste the target job description into the second box.
- Choose how many keywords to evaluate, then submit.
- Review the overall score and the missing keyword list.
- Update your resume, then run the check again.
- Download your results as CSV or PDF for tracking.
Article
Why ATS Compatibility Matters
Applicant tracking systems often rank resumes before a recruiter reads them. This checker converts your resume and a job description into comparable text signals, then estimates how easily standard parsers can extract skills, titles, dates, and quantified outcomes. The goal is not to “game” hiring, but to reduce accidental rejection caused by missing terminology, unclear section labels, or hard to parse layouts. Consistent wording also helps when recruiters search their database later. For best comparisons, paste plain text from your source file, keep acronyms spelled out once, and align job titles with the employer’s wording where appropriate.
How Keyword Coverage Is Estimated
The calculator extracts frequent role terms from the job description using unigram and bigram counts after removing common stopwords. It then calculates Keyword Match as matched_terms / evaluated_terms × 100. Because many roles use phrases like “data quality” or “client onboarding,” phrase matching is included, not just single words. You can raise the evaluated list from 10 to 60 to capture more niche tools and methods.
Formatting Signals That Affect Parsing
Many ATS engines struggle with tables, multi column designs, and decorative separators. The Formatting Score starts at 100 and applies penalties for column markers (tabs, pipes, long spacing), heavy rules (____ or -----), and special bullets. It also flags hints of headers, footers, or text boxes that may be flattened incorrectly. A higher score suggests your content will survive conversion from PDF to text more reliably.
Interpreting Readability and Length
Readability is estimated using a Flesch style formula based on words per sentence and syllables per word, then mapped onto a 0–100 scale. Scores near the middle usually indicate clear, skimmable bullets. The Length Score compares your word count with an ATS friendly range (roughly 350–900 words), rewarding concise detail while discouraging overly short documents that lack evidence or overloaded documents that bury key skills.
Using Results to Iterate Faster
Overall Score combines the components with weights: 55% keywords, 15% formatting, 15% readability, 10% section coverage, and 5% length. Section Coverage looks for headings such as Summary, Skills, Experience, Education, Projects, and Certifications, which improves extraction accuracy. Use the Missing Keywords list to add only relevant terms in context, then rerun the check. Track improvements by exporting CSV or PDF for each targeted role and version.
FAQs
Paste the text version of your resume. Use content from a text-based document, not a scanned image. Keep headings like Skills and Experience so the checker can detect sections.
Start with 30 for most roles. Increase toward 50–60 for technical jobs with many tools. Reduce to 15–25 for short postings to avoid noise from repeated generic terms.
No. Scores reflect text alignment and parsing risk, not your full fit. Use the results to remove avoidable screening friction, then rely on strong achievements and relevant experience.
The extraction is frequency-based. If a key requirement appears only once, it may rank lower. Raise the keyword count, or manually add the term to the job text for testing.
Use a single-column layout, standard headings, and simple bullets. Avoid tables, columns, text boxes, and long decorative lines. Save to a text-based PDF when exporting.
Yes. Run the check after each revision and download CSV or PDF outputs. Keep one file per job target so you can compare keyword coverage and overall score across versions.