Compute the two never-blended 0-100 scores (Search SEO and AI Visibility / GEO-AEO) from a set of findings, with severity-weighted category values, dynamic re-normalization of conditional modules, severity gating, and letter bands.
Compute the two never-blended 0-100 scores (Search SEO and AI Visibility / GEO-AEO) from a set of findings, with severity-weighted category values, dynamic re-normalization of conditional modules, severity gating, and letter bands.
Used by seo-orchestrator and the `score` command.
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Stars57
LicenseLICENSE
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skill mdSKILL.md2,897 B
docsSUMMARY.md298 B
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First seen on skills.sh
First recorded snapshot · 142 installs
SKILL.md
seo-score
Turns findings (conforming to schema/finding.schema.json) into the two scores. Full model in references/scoring-model.md — follow it exactly.
Steps
Group findings by the category each module maps to, per score. A finding contributes only to the score(s) in expected_impact.axis (search, ai, or both).
Category value = 100 × Σ(statusfactor × severity for scored findings) / Σ(severity), where statusfactor: pass 1.0, warn 0.5, fail 0.0. Exclude needsapi and notapplicable from both sums.
Active weights: drop conditional categories (e-commerce/local/international) whose modules produced no findings; re-normalize remaining weights to sum to their active total.
Score = Σ(category_value × weight) / Σ(active weight) for each of Search SEO and AI Visibility.
Severity gating: if any finding has severity: 5 and status: fail, cap the affected score at 40 and set capped: true.
Assign bands (A≥90, B≥80, C≥70, D≥60, F<60) and a one-line interpretation from the Search×AI quadrant.
6b. Coverage floor: report coverage (the % of the axis's always-on weight that carried a scored finding). Below 50% the axis comes back provisional: true with state: "partial" — quote the band and the coverage figure together, never the letter on its own.
M21 (AI discovery & agent endpoints — llms.txt, agents.md, UCP, agentic sitemap) weight is 0 — report it, never let it move the AI score.
Determinism
Prefer node "${CLAUDEPLUGINROOT}/scripts/score.mjs" --run <run-dir> (it reads <run-dir>/findings.json) or --findings <path> for a bare findings file, adding --vertical a,b, --multilingual and --environment production|preview|staging|local when the file carries no run context, so the number is reproducible and CI-checkable. --run takes a path, never the word latest: the score command resolves latest[:host] from <root>/<host>/latest.json first and passes the directory. If Node is unavailable, compute by hand following the same formula and note the fallback. Either way the math must match references/scoring-model.md.