/claude-seo-ai:geo

The AI-search (GEO/AEO) subset of the audit. Read-only.

$ARGUMENTS = <url|path> [flags]. --pages N, --render static|auto|js (default static) and --feed <path> (the Agentic Commerce Protocol product feed; it only does anything when the e-commerce vertical is active, and without it M18.agentic.acp_feed_errors stays needs_api — an unread feed is never a clean feed) are forwarded to the orchestrator and reach audit.mjs unchanged. --probe and --gsc-ai-export are handled here, not by audit.mjs: they are probes — reported, never scored (severity 0, excluded from the AI Visibility score and from every cap).

What to do

1. Invoke seo-orchestrator with the target and flags and the instruction "AI path only": it acquires the snapshot with the scripts (never WebFetch), runs the deterministic checks, and dispatches ai-search-geo-specialist with M6 (seo-entity-linking), M11 (seo-geo-answerblocks), M12 (seo-geo-factdensity), M14 (seo-ai-crawlers — access + Google AI-feature eligibility), M21 (seo-ai-discovery — llms.txt, agents.md, UCP, agentic sitemap), M22 (seo-agent-readiness); plus M5 (seo-schema-jsonld, via schema-generator) and M4 (seo-rendering, via technical-auditor) because they feed AI visibility.
2. Run the probe flows below only when their flag is present.
3. Present from report.json: the AI Visibility score (band + interpretation), the citability breakdown by category, needs_api / manual_review counts, the prioritized GEO/AEO actions, any probes section (clearly separated from the score), and the report.md path.
4. Lead with the one thing Google documents: a page reaches AI Overviews / AI Mode by being indexed and snippet-eligible in Search (M14 ai_eligibility). Remind the user that M21 — llms.txt and the other discovery files — is reported at weight 0 (Google Search ignores llms.txt; vendor support is partial). Never write files; offer /claude-seo-ai:fix for the safe AUTO fixes (e.g. the AI-crawler robots.txt preset).

--probe "<question>" — web-search presence (repeatable)

A rough proxy for "does this site show up when someone asks this?", built from one search engine at one moment. It is not citation data.

1. For each --probe question, call WebSearch with the question verbatim. Do not rewrite it, do not add the brand name, and do not run extra queries the user did not ask for.
2. Read the organic results in order and build one JSON object — rank is the 1-based position in that result list, url and title are copied verbatim, and host is the audited site's hostname:

   { "host": "example.com",
     "queries": [ { "query": "best sustainable running shoes",
                    "results": [ { "rank": 1, "url": "https://…", "title": "…" } ] } ] }
   

3. Pipe it to the reporter over stdin (nothing is written to the user's project); --out saves the probe inside the run directory, which is where report.mjs picks it up as probes.web_search:

   node "${CLAUDE_PLUGIN_ROOT}/scripts/probe-report.mjs" --host <host> \
     --out "<run_dir>/probes/web-search.json" [--history <dir>] <<'JSON'
   { "host": "…", "queries": [ … ] }
   JSON
   

   --results <file.json> reads the same JSON from a file instead of stdin.
4. It returns presence rate (queries where the host appears), best rank, the competitor-host tally, and a trend against earlier probe runs stored for that host. Report it under probes.web_search and quote its disclaimer field verbatim — echo the string the script emits (DISCLAIMER in scripts/probe-report.mjs, also written into report.json and report.md), never a paraphrase of it, so the model's prose and the machine-generated line beside it cannot disagree. Today that string reads: "Web-search presence for these questions. This is NOT AI Overview, AI Mode, ChatGPT, or Perplexity citation data — no vendor exposes that. Indexing/retrievability is a documented precondition for Google AI features; presence here is necessary, not sufficient."
5. The finding is M14.probe.web_presence, severity 0, method web_search_probe. Never let a probe change the AI Visibility score, and never describe a rank as a ranking measurement — WebSearch results are personalized, localized, and volatile.

--gsc-ai-export <csv> — Search Console generative-AI import

1. The user exports the Generative AI performance report from Search Console by hand; there is no API for it. Pass the file:

   node "${CLAUDE_PLUGIN_ROOT}/scripts/gsc-ai-import.mjs" --csv <file.csv> [--host <host>] [--history <dir>] \
     > "<run_dir>/probes/gsc-generative-ai.json"
   

   The script prints the import and writes nothing itself, so redirect it into the run's probes/ directory — that is what makes report.mjs report it under probes.gsc_generative_ai at data tier 2.
2. English and Spanish column headers are both recognized. The report carries impressions only — no clicks, no citations, no per-answer attribution — so the import reports impressions and nothing more.
3. Report it under probes.gsc_generative_ai. Findings, all severity 0: M14.gsc_ai.impressions_present (the export shows generative-AI impressions for this property), M14.gsc_ai.zero_impressions (the export parsed but contains none), M14.gsc_ai.unavailable (needs_api — no export supplied, or the file could not be parsed; the default state).
4. Say plainly what the number is not: impressions in Google's generative-AI surfaces are not citations, not clicks, and not a score input.

Both probe sections live beside the score, never inside it. If the user asks "did my AI visibility go up?", the honest answer is that these are proxies and the score is a structural assessment — the two are reported separately for exactly that reason. Respond in the user's language (EN/ES).