ai-visibility-monitorlisted

# 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 <= observed_at` for every cell; never add, remove, reword, translate, or silently substitute a query during a run. 3. Declar