Answer Engine Visibility

---
name: answer-engine-visibility
description: Audit and improve whether AI-generated answers and answer engines can access, understand, cite, and accurately recommend the product: crawler and robots policy, entity clarity, citable source content, machine-readable knowledge assets, agent readiness, and repeatable monitoring of observed answers. Use for AI search, LLM visibility, and AI Overview questions; use search-health for classic crawl and index problems.
license: MIT
metadata:
  version: "2.0.0"
  author: MTEnt
---

# Answer Engine Visibility

Optimize for useful, supportable information across humans and machines. No technique guarantees citation or recommendation in a model-generated answer.

If `.agents/marketing-context.md` exists, read it first; it is the truth file every skill in this hub shares, and its evidence states are constraints. Read [ai-discovery-system.md](references/ai-discovery-system.md) for the four problems, the visibility audit, useful content, citation versus recommendation, agent readiness, and monitoring.

## Operating contract

1. Define target audience questions, intended product fit, source pages, current visibility evidence, and business outcome.
2. Verify current crawler, publisher, product, and robots guidance from official sources.
3. Separate access, understanding, citation, recommendation, and measurement.
4. Improve source quality and entity consistency before adding speculative machine-only files.
5. Test retrieval and observed answers over time, preserving prompt, surface, date, locale, and source evidence; a single answer is not a baseline.

Do not manufacture statistics, consensus, third-party mentions, awards, customer proof, or citations.

## Return

Question set, visibility evidence, access and entity findings, content and evidence gaps, page specifications, optional machine-readable assets with support rationale, monitoring design, and a no-guarantee statement.