Hire an AI search visibility reviewer

Everything it can hand back

Your task sets the actual outputs. The specialist picks the methods that cover them.

• A scorecard per task that shows if an agent can find it, start it and finish it, and where it drops.
• A step-by-step friction map marked Pass, Degraded or Fail, with the cause of each failure.
• A fix list, each tied to a stuck point. Fixes are form markup, field changes or flow changes.
• WebMCP markup for forms that fit it, or a script outline for dynamic actions.
• A test sheet to run with a real browsing agent before and after your changes.

How it does the job well

• Audit journeys, not pages. Keep this separate from search ranking and from being cited in AI answers, which need their own checks.
• Baseline before any change. Without a before result, an improvement cannot be shown.
• Fixes are proposals. Do not change a live site. Do not let any agent book, buy or send without the user's own confirmation step.
• Separate what can be tested today from what is only proposed.
• Always audit actual task flows. Don't audit pages — audit user journeys: book a room, submit a lead form, create an account. Agents care about tasks, not pages.
• Never conflate WebMCP with AEO/SEO. Getting cited by ChatGPT is wave 2. Getting a task completed by a browsing agent is wave 3. Treat them as separate strategies with separate metrics.
• Test with real agents, not synthetic proxies. Task completion must be validated with actual browser agents (Claude in Chrome, Perplexity, etc.), not simulated. Self-assessment is not audit.

Mistakes it avoids

• Generic copy that could fit any brand.
• Comparing periods or campaigns that are not comparable, such as different dates, budgets or tracking.
• Calling a test winner before there is enough data.

What a good result looks like

An example, not your task: You want AI browsing agents to be able to book, order or sign up on your site.

• Task scorecard. One row per task showing if an agent can find, start and finish it, and where it drops.
• Friction map. Each step of each task marked Pass, Degraded or Fail, with the cause.
• Fix list. Changes in order of value, each linked to the task it unblocks.
• Form markup draft. Your forms with tool name, description and field labels added, ready for your developer to review.
• Test sheet. A before and after table to fill in when you run each task with a real agent.

How the work is checked

• Every claim traces to the brand brain or supplied evidence.
• Each recommendation states the expected effect, the cost and how it will be measured.
• Copy fits the platform's length limits and the brand voice.
• States which checks passed, failed or could not run, and names missing inputs.

These checks have not been run against a model yet. Your AI uses them to check its own work.

A second opinion

Before it hands back, your AI reviews the draft the way Neil Patel would. This is a way of reviewing the work. It does not mean they wrote or endorse it.

Capabilities

• List the tasks. It picks the 3 to 5 tasks of most value and maps each from start address to success page.
• Check each step. It reads your code and steps for named fields, labels, native inputs, walls such as CAPTCHAs and sign-ups, and any WebMCP markup.
• Map the friction. For each task it marks every step Pass, Degraded or Fail, and names the cause.
• Draft the fixes. It picks form markup where a plain form exists and a script where the action is dynamic, and swaps blocking widgets for plain inputs.
• Plan the retest. It writes a test sheet so you measure before and after with real agents. Completion rate is counted only from real tests.

How the role works

1. Checks your task, context and available tools.
2. Picks the methods needed and prepares a work plan.
3. Carries out supported tasks, with each method's limits and review gates.
4. Checks the real deliverables and corrects supported faults.
5. Hands back files, evidence and unresolved gaps.

Your specialist's working setup

Download the base specialist without a task form or account. README.md and SETUP.md explain how to use the folder. SOUL.md defines its scope. INTAKE.md asks missing questions inside your AI. PLAYBOOK.md, SKILLS.md, BEST_PRACTICES.md, CHECKS.md and HANDOFF.md guide the work and its checked deliverables.

Complete associated skills, supporting files and licences are included. Choose only the methods needed for the actual task. Keep context, working files and accepted feedback in your AI project.

Use this specialist through MCP or the API

Call prepare_specialist with role_id marketing-agentic-search-optimizer only. No task is required to load it. Your AI asks what job you need done, what you want back and which relevant context it can use.

For task-led hiring, call find_specialist, explain the suggested specialists and let the user accept or change the team.

Complete ZIP: Download base specialist. API: GET /api/v1/specialists/marketing-agentic-search-optimizer/pack. The host AI supplies its own model, tools and execution; a pack does not connect accounts or run a task.

Inputs and helpful context

The specialist starts from the task brief. Only active methods require their inputs.

For the primary method, when used

• The 3 to 5 tasks that matter most, such as book, buy, register, subscribe or contact.
• For each task, the starting address and the steps a person takes.
• The page code of each form or step, from view source (optional, needed for markup fixes).
• Your own test results with a browsing agent, if you ran one (optional).
• Your site platform, such as Shopify or WordPress (optional).

Helpful context (optional)

• The visible text and the code of each form.
• A screen recording of your own test run.
• A list of steps that need sign-in or payment, since agents should not do these alone.

Tools and access

• A real browsing agent, such as Claude in Chrome: Only a real agent can show if a task can be finished. The chat AI reviews code and cannot. Claude in Chrome runs in the Chrome browser and needs a paid Claude plan, so check the current terms.
• Claude, ChatGPT or Gemini: Any of them can read your form code and write the fixes.

Functions and deliverables

• AI search visibility reviewer: Check if an AI browsing agent can book, buy or sign up on your site. Get a task scorecard, the steps where it gets stuck and fixes.
  • A scorecard per task that shows if an agent can find it, start it and finish it, and where it drops.
  • A step-by-step friction map marked Pass, Degraded or Fail, with the cause of each failure.
  • A fix list, each tied to a stuck point. Fixes are form markup, field changes or flow changes.
  • WebMCP markup for forms that fit it, or a script outline for dynamic actions.
  • A test sheet to run with a real browsing agent before and after your changes.
  Source: Michael Sitarzewski and Agency Agents contributors.
• Search and AI answer visibility auditor: Share a website. Get search and AI access checks, observed answer results and ready-to-use fix files. Connected site edits need review.
  • A crawl report of what was and was not reachable
  • Results from real assistant questions
  • The top five fixes in order
  • A corrected robots.txt and an llms.txt
  Source: Anthropic.
• AI search visibility analyst: Share a page, topic and search goals. Get content and access improvements, source checks and a plan to track real AI mentions.
  • A check of today's AI answers on your topic
  • Page edits that make content easy to quote
  • Schema markup suggestions
  • A tracking list of prompts to monitor
  Source: Corey Haines.
• Site search basics checker: Paste your site address and page list. Get a short fix list for duplicate titles, a missing sitemap and dead-end 404 pages.
  • Duplicate titles and descriptions grouped by pattern
  • Whether a sitemap exists, is submitted and how many pages are indexed
  • Each 404 with a suggested destination
  • A score out of 100 and a fix list ranked by pages affected
• Brand profile builder: Share your website. Get a reusable profile covering your products, customers, voice and approved claims, with sources.
  • A folder of eight plain files, from a start file for your AI to a claims list and a change log
  • A tag on every fact, and every claim marked approved, pending or banned
  • A two-sentence diagnosis of your biggest gap, and a list of what's missing and who can answer it
  • A short slice for your next step, if you name one
• Neil Patel: Plan search content using Neil Patel’s recorded approach. Start with customer questions, review useful gaps and set a realistic publishing and measurement plan.
  • Your problem worked on the way Neil Patel would: their questions, their method, their push-back
  • The answer or the work, in Neil Patel's voice, using their ideas by name
  • Anything that goes beyond their public record, marked
  • One suggested next step

Tasks you can use it for

• You want AI browsing agents to be able to book, order or sign up on your site.
• Your booking or checkout uses a custom date picker, a pop-up widget or a single-page app.
• You added a CAPTCHA or forced sign-up and wonder what it does to AI agents.
• You were told to prepare for WebMCP and want a plain list of what to change.

Limits to check before starting

• You want AI tools to mention your brand in their answers. Use the AI citation visibility planner.
• You want AI crawlers allowed and an llms.txt file. Use the AI crawler and llms.txt setup agent.
• You want to see how people, not agents, struggle with a form. Use the UX Researcher agent.
• You need your live site changed for you. This writes the markup and fixes. You or your developer apply them.

Best way to use this role

Give it one clear outcome, the actual files and a definition of done. Keep the work in one AI project so it can reuse checked context and earlier files.

• Add the pack to your AI as a skill. In a plain chat, paste SKILL.md first.
• List your 3 to 5 tasks with the start address and steps. Paste the form code for each if you can.
• Say "Audit these tasks for AI browsing agents."
• Read the scorecard. Anything marked "from the code, not tested" needs a real test.
• Run each task with a real browsing agent, such as Claude in Chrome. Write down where it stops. Do this before you change anything, so you have a baseline.
• Apply the fixes, or hand them to your developer, then run the same tests again. Keep your normal checks and sign-in rules. Markup does not replace them.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/marketing-agentic-search-optimizer for its role instructions and pinned sources.

This is a prepared AI role. No task has run. Your AI carries out authorized work using its available tools and reports the actual deliverables and checks.