/claude-seo-ai:geo

$ARGUMENTS

<url|path> [flags]

--pages N

--render static|auto|js

static

--feed <path>

M18.agentic.acp_feed_errors

needs_api

audit.mjs

--probe

--gsc-ai-export

audit.mjs

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)

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:
   json

   { "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

   :
   bash

   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:
   bash

   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.