Last updated: July 2026
Your Skill Vector match score is deterministic: the same resume and job description always produce the same number. It is computed by a pure-Python engine — AI (Claude) never influences the score. This page documents exactly how it works so you can verify every point.
Two texts: your resume and a target job description. Both are cleaned and checked for minimum substance before scoring. Nothing else affects the number — not your name, not the time of day, not any AI model.
The engine extracts the concrete skills a job requires (tools, languages, certifications) from a curated taxonomy of 360+ skills and their aliases, then measures what share of them your resume demonstrates. “JS” and “JavaScript” count as the same skill.
Both documents are converted into multilingual sentence embeddings, and the engine measures how close their meaning is. This credits relevant experience even when the exact words differ — “built predictive models” relates to “machine learning” without a literal keyword match.
The final score is a weighted blend of the two signals, clamped to 0–100:
| Job language | Skill Match | Semantic Similarity |
|---|---|---|
| English | 55% | 45% |
| Non-English | 60% | 40% |
For non-English job descriptions the skill taxonomy detects fewer skills, so the engine weights the skill signal slightly higher and applies a confidence adjustment to avoid unfairly penalizing candidates whose real coverage exceeds what the taxonomy can detect. This is why the weights differ by language — it is deliberate, published, and consistent.
Claude powers the optional improvement tools: rewriting bullet points and full resumes, generating cover letters, career coaching, and mock-interview scoring. These are creative aids you choose to use.
AI never computes, adjusts, or influences your match score, your skill gaps, or your usage limits. Those are 100% deterministic engine outputs. If AI is unavailable, your score is unaffected.
The score reflects text-based signal only — it cannot see design, PDF layout rendering, or achievements you did not write down. Skill detection depends on the taxonomy, which is continually expanded but not exhaustive. The score is a decision aid, not a hiring guarantee.
Questions about the methodology? The engine is documented in the open repository.