audit
Audit a website or web codebase for SEO and AI-search (GEO/AEO) — produces two independent 0-100 scores (Search SEO + AI Visibility) plus a prioritized,…
Audit fact density and sourcing on a page — measure statistic/number density per passage, detect proprietary/original data, count outbound citations to authoritative sources, and flag claims made without a supporting stat or source. Module M12. Feeds the AI Visibility score.
$ npx -y skills add Hainrixz/claude-seo-ai --skill seo-geo-factdensity --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/seo-geo-factdensityContext preview
The summary Claude sees to decide when to auto-load this skill.
Audit fact density and sourcing on a page — measure statistic/number density per passage, detect proprietary/original data, count outbound citations to authoritative sources, and flag claims made without a supporting stat or source. Module M12. Feeds the AI Visibility score.
name: seo-geo-factdensity description: Audit fact density and sourcing on a page — measure statistic/number density per passage, detect proprietary/original data, count outbound citations to authoritative sources, and flag claims made without a supporting stat or source. Module M12. Feeds the AI Visibility score. Advisory-only; never fabricates statistics or sources. allowed-tools: Read, Grep, Glob, WebFetch, Bash
Generative engines preferentially cite passages that are concrete, quantified, and attributable. This module measures how "citable" the page's prose is — facts, numbers, original data, and authoritative outbound links — not vocabulary. AI retrieval/citation context: `references/ai-crawlers.md`.
Work from the PageSnapshot named in your dispatch envelope: read `parsed` from `<run_dir>/pages/<slug>.json` (`anchors[]` for outbound links, `text_sample`) and the HTML file for passage-level counts; Grep `pages/<slug>.html` for verbatim evidence; site artifacts live in `<run_dir>/site/{robots.json,sitemaps.json,discovery.json}`. Deterministic findings already emitted by `audit.mjs` are listed in `<run_dir>/findings.deterministic.json` — do not re-emit those ids; add model-judged findings only. If invoked directly with a URL/path and no snapshot exists, first run `node "${CLAUDE_PLUGIN_ROOT}/scripts/snapshot.mjs" <target> --out "${CLAUDE_PLUGIN_DATA}/runs"` and use the printed snapshot path.
Working from the PageSnapshot (`parsed_rendered` when `render.used` is not `none`, else `parsed`): 1. **Statistic/number density** per passage: tokenize the main content into passages (paragraph / `<li>` / heading-bounded block) and count numeric tokens — figures, percentages, dates, quantities, ranges. Flag long passages of pure assertion with zero numeric support. 2. **Proprietary/original data**: detect first-party-data signals — patterns like "our study", "our survey", "our data", "we analyzed", "we surveyed", "in our test", "internal data" — and note whether such claims are backed by a method/sample, a table, or a chart. 3. **Outbound citations**: count outbound links from the main content to authoritative sources (standards bodies, primary research, official docs, `.gov`/`.edu`, named publications); distinguish them from internal/nav/affiliate links. 4. **Claim-without-source flags**: detect strong factual or comparative claims ("the most", "fastest", "studies show", superlatives, hard numbers) that carry no inline citation or data reference, and mark each as a candidate for sourcing.
ADVISORY only — this module proposes nothing it would write. It produces a list of (a) claims that should carry a statistic or citation, (b) passages where an original-data callout (table, "our data" box, methodology note) would raise citability, and (c) unsupported superlatives to soften or source. The tool will **NOT** fabricate statistics, sample sizes, study results, or source URLs. Where a value is missing, it emits a clearly-marked `TODO` placeholder for the user to fill — never an invented number. (Findings here are `fixable: advisory` per the finding schema.)
Emit findings per `schema/finding.schema.json`; axis `ai`; `fixable: advisory` throughout. **Severity policy**: 5 is reserved for catastrophic, eligibility-killing facts at site/template scope; 4 major · 3 moderate · 2 minor · 1 cosmetic · 0 informational — and only an `established` severity-5 `fail` in an active category can cap a score (`references/scoring-model.md`). Nothing in M12 is `established`, so nothing here ever caps.
Each finding: `evidence.observed` quotes the exact passage/claim from the page; `verification.reproduce` is the runnable count above; `expected_impact` is banded + confidence-tagged (no naked %).
The SEO + AI-search (GEO/AEO) optimization toolkit for Claude Code — two-score audit + opt-in fixer. Built for 2026-2027.
Repo: Hainrixz/claude-seo-ai
Audit a website or web codebase for SEO and AI-search (GEO/AEO) — produces two independent 0-100 scores (Search SEO + AI Visibility) plus a prioritized,…
Compare a site against its own baseline, against a staging deployment, against up to four competitors, or against the pages that already answer a query…
Opt-in fixer (the /claude-seo-ai:fix command). Applies the safe, deterministic SEO/AI-search fixes from a persisted audit to the user's site — meta…
Analyze and score only a page's AI-search visibility (GEO/AEO) — answer extractability, fact density, AI-crawler access and Google AI-feature snippet…
Recompute and display the two scores (Search SEO + AI Visibility) from a persisted audit run, without re-crawling. Use to re-show or refresh the scores after…
Audit how well a page can be operated by AI agents and agentic browsers — semantic interactive controls (<button>/<a href> instead of div/span click handlers),…