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

1. 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).
2. 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.
3. Active weights: drop conditional categories (e-commerce/local/international) whose modules produced no findings; re-normalize remaining weights to sum to their active total.
4. Score = Σ(category_value × weight) / Σ(active weight) for each of Search SEO and AI Visibility.
5. Severity gating: if any finding has severity: 5 and status: fail, cap the affected score at 40 and set capped: true.
6. Assign bands (A≥90, B≥80, C≥70, D≥60, F<60) and a one-line interpretation from the Search×AI quadrant.
7. 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, "interpretation": "Citable structure missing; add answer blocks and schema.", "categories": [ ... ] } }