ResuRank
Evaluate resume-to-job fit locally, without sending documents anywhere.
ResuRank is an open-source desktop application for evaluating resume-to-job fit without sending either document to a third party. Upload your resume PDF once and ResuRank stores it locally. Then paste any job description and you get a 0–100% match score within seconds, computed by two independent methods that complement each other: semantic similarity (capturing meaning through vector embeddings) and keyword similarity (TF-IDF with rarity weighting). The two are blended with a divergence adjustment that protects against false positives. Everything is local. The ~25 MB embedding model is downloaded once on first launch and cached in your user data directory. Your resume PDF, settings, term boosts, and job descriptions never leave your machine. No cloud, no API keys, no usage limits, no telemetry.
Features
- • Hybrid scoring — semantic embedding (70%) + keyword TF-IDF (30%) with divergence adjustment
- • Local inference — runs entirely on your device, no internet required after first run
- • Term boosting — weight specific keywords to reflect skills you want to emphasize
- • Critical missing keywords — flag must-have terms; their absence from your resume reduces the score with adjustable importance tiers
- • Stopword exclusion — customize the word list ignored during scoring
- • Score breakdown — see embedding, TF-IDF, overlap bonus, and divergence penalty separately
- • Language detection — warns when a job description appears to be in a different language
- • PDF resume parsing — upload once, reuse for every job
- • Auto-update — signed updates delivered via electron-updater on macOS and Windows
- • Score tiers — Poor fit / Fair / Good / Great fit at a glance