Ai Visibility Monitor

AI Visibility Monitor

Produce a hash-pinned Visibility Run from immutable research, a formal frozen Query Corpus, repeated raw answer captures, retrieval traces where the surface exposes them, and citation reviews. This skill measures what was observed; it does not prescribe changes or claim why a metric moved. Legacy 1.0.0 runs remain readable; create new work with schema 2.0.0.

Required inputs

• Require a contract-valid Research Pack produced by ai-search-research; bind its bundle-relative path and SHA-256 into every run.
• Require a valid query-corpus.json whose Research Pack hash matches, whose frozen_at precedes every observation, and whose selected query text, locale, engine, surface, entities, and fact_ids resolve. Do not invent hidden fan-out queries.
• Require dated raw answer captures for observed cells. Preserve inaccessible, blocked, unavailable, and error states as explicit null-answer observations.
• For a comparison, require the prior Visibility Run as an immutable hash-pinned artifact.
• Read [measurement-protocol.md](references/measurement-protocol.md) before collecting, scoring, or comparing observations.

Procedure

1. Validate the complete Research Pack and its semantic provenance before measurement.
2. Freeze the query corpus before collection. Record timezone-aware frozen_at, hash it, and require frozen_at mean ${CLAUDE_PLUGIN_ROOT} in Claude Code. In Codex, read .seo-suite-runtime.json beside this SKILL.md when present and use its suite_root value; otherwise use the absolute repository checkout. Run python "/scripts/validate_ai_visibility_monitor.py" validate-run --bundle and close all critical or important findings. Put minor findings in backlog.

Metric boundaries

• mention_rate: share of eligible observed answers that explicitly mention one Research Pack entity.
• citation_rate: share of eligible observed answers containing at least one citation.
• cited_source_share: share of distinct canonical sources per observed answer matching one declared canonical source URL.
• answer_accuracy: share of all declared fact-to-observation checks assessed correct; partial and unverifiable remain in the denominator.
• referral_rate: use only when a hash-pinned referral envelope binds a supported raw event export. Recompute the numerator and denominator from eligible ai/non_ai events inside the declared window. Otherwise omit it.

Do not combine these metrics into a universal visibility score. Do not compare percentages whose corpora, access states, time windows, locales, or metric definitions are not comparable.

Ownership boundaries

• Route new query discovery, source collection, or Research Pack repair to ai-search-research.
• Route direct-answer and extractability audits to seo-aeo.
• Route entity, evidence, citation-suitability, and documented engine-control audits to seo-geo.
• Route a validated audit and any needed baseline evidence to seo-action-plan before implementation; this monitor does not rewrite, optimize, or approve a change.

Never bypass authentication, bot controls, rate limits, paywalls, or unavailable product access. Record the access state and continue with the observable cohort.

Severity and review rule

• critical: the run would be materially false, fabricated, unsafe, or unusable.
• important: provenance, denominator, raw-evidence binding, comparison integrity, or ownership is materially incomplete or misleading.
• minor: a non-blocking clarity or maintainability improvement.

Close critical and important findings. Backlog minor findings. Review each candidate hash at most twice.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: oegeyilmaz9
• Source: oegeyilmaz9/seo-aeo-geo-ultimate
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.