AI Visibility Audit

Goal

Find the work most likely to get a brand named or cited in AI answers about its market, and explain it so a non-expert can act on it. Research broadly; recommend selectively. The report leads with one to three recommendations, each tied to the answers and cited pages that justify it.

This skill reads saved answers, adds the pages behind them, and decides what to do. For demand research alone, use ai-prompt-research.

Project context

The project-context tools are free and shared with the app and other agents.

1. External MCP clients: resolve the project with list_projects, ask only if the match is ambiguous, then call get_project_context. In SAM, use the current project and context already injected into the conversation; SAM has no get_project_context tool and needs no project selection or connection setup.
2. This skill needs business_overview, the website and the main competitors. If the overview is empty, infer it from the site, confirm it in one question, and save it with update_project_context.
3. Read get_ai_visibility_tracker. Its prompts, competitors, engines, market and latest runs decide which path below applies. Check the tracker's own brand, the brands row with own: true. Its name is the project name. If that is not how people write the brand (for example a project named "Acme website"), answers that name the brand are not counted as mentions and brand questions are not treated as branded. Ask the user to rename the project in the app before collecting or interpreting answers.
4. Reuse research-log findings under 30 days old for discovery. A claim that drives a recommendation still needs evidence from a run or a page read during this audit.
5. On finish, write back the pages the report names with addKeyPages and append { updates: [{ appendResearchLog: { summary: "AI visibility audit: <run id>. Verdict: <conclusion>" } }] }.

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