ResuRank

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. Paste any job description and get a 0–100% match score within seconds, computed by @resurank/scoring — two independent methods that complement each other: semantic similarity (vector embeddings) and keyword similarity (TF-IDF with rarity weighting), blended with a divergence adjustment that protects against false positives.

Everything runs locally. The ~25 MB embedding model is downloaded once on first launch and cached in your user data directory. Your resume, settings, term boosts, and job descriptions never leave your machine — no cloud, no API keys, no usage limits, no telemetry.

It also ships a companion MCP server, so AI assistants like Claude Desktop can run the same scoring engine directly — see ResuRank MCP.

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 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
• MCP server — connect ResuRank to AI assistants via Model Context Protocol