hainrixz/claude-seo-ai

seo-score

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.

First seen Jun 2, 2026

Installation

$ npx skills add hainrixz/claude-seo-ai --skill seo-score

Summary

  • 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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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
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Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 57
License LICENSE
Default branch main
Open issues 0
Status Active

Skill metadata

Parsed from SKILL.md frontmatter.

Allowed toolsRead, Bash

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 2,897 B
  • docs SUMMARY.md 298 B

History

  1. First seen on skills.sh
  2. 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

  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(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.
  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.

  1. 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.

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": [ ... ] } }