audit
Audit a website or web codebase for SEO and AI-search (GEO/AEO) — produces two independent 0-100 scores (Search SEO + AI…
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`
Claude Code
$ npx -y skills add Hainrixz/claude-seo-ai --skill seo-score --agent claude-code
Codex
$ npx skills add Hainrixz/claude-seo-ai --skill seo-score -a codex -y
Cursor
$ npx skills add Hainrixz/claude-seo-ai --skill seo-score -a cursor -y
Gemini CLI
$ npx skills add Hainrixz/claude-seo-ai --skill seo-score -a gemini-cli -y
GitHub Copilot
$ npx skills add Hainrixz/claude-seo-ai --skill seo-score -a github-copilot -y
OpenCode
$ npx skills add Hainrixz/claude-seo-ai --skill seo-score -a opencode -y
Windsurf
$ npx skills add Hainrixz/claude-seo-ai --skill seo-score -a windsurf -y
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. user-invocable: false allowed-tools: Read, Bash
<!-- Portions Copyright (c) 2026 Enrique Rocha (@soyenriquerocha) - see LICENSE for original authors · tododeia.com · id er-d03758e1 -->
Turns findings (conforming to `schema/finding.schema.json`) into the two scores. Full model in `references/scoring-model.md` — follow it exactly.
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. 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. 7. M21 (AI discovery & agent endpoints — llms.txt, agents.md, UCP, agentic sitemap) weight is 0 — report it, never let it move the AI score.
Prefer `node "${CLAUDE_PLUGIN_ROOT}/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`.
{ "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": [ ... ] } }The SEO + AI-search (GEO/AEO) optimization toolkit for Claude Code — two-score audit + opt-in fixer. Built for 2026-2027.
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