| name | seo-score |
| description | 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. |
| allowed-tools | Read, Bash |
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(status_factor × severity for scored findings) / Σ(severity), where status_factor: pass 1.0, warn 0.5, fail 0.0. Exclude needs_api and not_applicable 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.
- M21 (llms.txt) weight is 0 — report it, never let it move the AI score.
Determinism
Prefer scripts/score.mjs (run via Bash with the findings JSON) so the number is reproducible and CI-checkable; 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.
Output
{ "search_seo": { "value": 78, "band": "C", "capped": false, "interpretation": "...", "categories": [ {"name":"Indexability & Crawl","weight":22,"value":91,"active":true}, ... ] },
"ai_visibility": { "value": 64, "band": "D", "capped": false ...
