seo-scorelisted

# 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 ```json { "search_seo": { "value": 78, "band": "C", "capped": false, "int