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 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. Declare planned/completed repeats, confidence method, confidence level, and fresh-session policy. Collect every query/engine/surface/locale/repeat cell without choosing the most favorable answer.
4. Record observed_at, access state, disclosed model, conversation turn, user location, device, authentication/personalization state, retrieval mode, collection method, and either a hash-pinned raw answer or access-attempt receipt.
5. Preserve only actually disclosed executed/grounding queries and consulted sources. Keep consulted sources distinct from visible citations. Never reconstruct hidden retrieval behavior.
6. Preserve every cited URL exactly as observed, derive its canonical form separately, and review each citation-to-claim link with a hash-pinned support verdict. Citation presence is not claim support.
7. Resolve mentioned entities only when a supplied Research Pack name or alias appears in the raw answer. Do not infer mentions from citations alone.
8. Score only explicit metrics using the canonical versioned definition_id and exact definition for that metric, with visible numerators, denominators, sample size, repeat count, confidence interval or a declared not-estimated state, results, and uncertainty notes. Missing access is not a negative result.
9. Check every fact declared for every observed corpus cell/repeat against Research Pack ground truth. Automatic correct requires both the extracted claim and complete normalized answer to equal the accepted value; bind every other verdict to a hash-pinned review artifact.
10. For comparisons, verify prior hash, identity, chronology, Research Pack and corpus hashes, repeat profile, metric definition, score target, access profile, and referral source/method/window duration. Mark changed cohorts non-comparable and explain the warning.
11. Describe movement as observational drift. Never state or imply that an optimization, publication, schema change, crawler setting, or other intervention caused movement.
12. Create schema 2.0.0 visibility-run.json with stable IDs, hash-pinned artifacts, explicit limitations, and no optimization recommendations.
13. Let <suite-root> 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 "<suite-root>/scripts/validate_ai_visibility_monitor.py" validate-run <artifact> --bundle <bundle> and close all critical or important findings. Put minor findings in backlog.