Most resume scoring tools are black boxes. Ours isn't. Here's the full recipe — the real weights straight from the engine, not a marketing approximation.
The number is computed, not guessed
The Merit Score is fully deterministic. Every point comes from evidence the engine can show you — keyword matches, parsed dates, bullet stats, vendor parse simulations. A language model writes the explanation (strengths, gaps, next steps), but it never sets the number. Re-score the same resume against the same job description and you get the exact same score, every time.
The nine axes
The overall score is a weighted blend of nine evidence-anchored axes (weights sum to 100%):
Skills & keywords
25%
Experience fit
18%
Quantified metrics
13%
Action verbs
11%
Semantic coverage
10%
Seniority
7%
ATS format
6%
Domain / industry
5%
Title fit
5%
| Axis | Evidence source |
|---|---|
| Skills & keywords | JD-weighted taxonomy match + synonym / implication expansion |
| Experience fit | Per-stack years-of-experience diff + title & seniority trajectory |
| Quantified metrics | % of bullets with numbers / % / $ (deterministic bullet analysis) |
| Action verbs | Strong-verb starts, passive-voice & weak-phrase flags |
| Semantic coverage | JD-requirement vs resume TF-cosine, section-weighted |
| Seniority | Title / level match vs JD seniority expectation |
| ATS format | Nine-vendor parser simulation + file inspector signals |
| Domain / industry | Domain background vs JD context (with anti-contamination gates) |
| Title fit | Most-recent title vs the target role title |
The two highlighted axes — keywords at 25% and experience fit at 18% — decide almost half the score between them. The two writing axes, metrics and verbs, add another 24%.
Relevance gating: fit, not polish
A beautifully formatted resume for the wrong job should not score well — that's the design goal behind the gating, and it's also why diagnosing a silent job search starts with targeting, not polish.
Smooth ceilings, no round-number clustering
Evidence-derived hard ceilings cap the score when something fundamental is missing — a must-have skill, or a years-of-experience gap. Those ceilings are interpolated along smooth curves rather than snapping to a handful of discrete values, so two different resumes don't collapse onto the same round number.
What this means for you
Two resumes with the same keyword overlap can still diverge by 10+ points if one quantifies its impact, leads with strong verbs, or aligns more closely on seniority. The free tool breaks the score down axis by axis — with an evidence drawer behind every number — so you can see exactly which axis is dragging you down, and fix that one first.
Written by
MeritSlate Team
The team building MeritSlate's deterministic scoring engine — nine evidence-anchored axes, a nine-vendor ATS parse simulation, and every point backed by receipts.