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Get your CV ATS score from a PDF or DOCX in seconds, see the highest-impact fixes first, match keywords, and build an ATS-clean resume without uploads, accounts, or LLM calls.
Pick a resume file from your device.
Text extraction and scoring run in this browser.
See score, breakdown, keywords, and exportable notes.
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The code is open source under the MIT license, and the product promise stays narrow: local document parsing, deterministic scoring, no account wall, and no AI model call hidden behind the interface.
PDF and DOCX text is extracted in the browser with client-side libraries.
Scores come from repeatable checks for parseability, sections, format, and content signals.
MIT-licensed. Read the scoring checks, parser, and export flow, open an issue, or send a pull request.
A fast heuristic check for parser-friendly text, contact details, sections, dates, bullets, and measurable impact.
No. PDF and DOCX files are processed locally in your browser and are not sent to a server.
No. The score is deterministic and does not use LLMs, model calls, accounts, or API keys.
Both work. Text-based PDFs give the clearest page-count and parseability signal.
It estimates how well a parser will read your document. It is a heuristic, not a prediction of a recruiter’s decision or of any employer’s system, and it has not been validated against hiring outcomes. Other checkers give the same file different numbers because each one measures something different.
There is no model and no training data, so there is no dataset to be biased. The rules still carry assumptions, such as the section headings and CV conventions they accept, and the writing-style check covers English CVs only and can flag good writing. It is not an AI detector. Nothing is decided about you: each point shows the rule behind it, and you decide what to change.