Hire an AI identity graph designer

Everything it can hand back

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

• A match plan with the fields to compare, how to clean each, and how heavily to weigh it
• Three decision bands, which are link automatically, review, or treat as new
• A review and undo process for merges and splits
• Sample test pairs to try on your own data before you trust the rules
• Privacy notes and a list of what is missing

How it does the job well

• Never merge on "these look alike". Each match needs per-field evidence and a reason a person can read.
• A missing field never raises a score. Empty values after cleaning count as no evidence.
• Prefer a proposal that a person or second check reviews over a direct merge, unless the user said the cost of a wrong merge is low.
• Build in a split. Every merge needs a way to undo it and a record of who decided.
• Keep each customer or company's records apart from others. Never mix records across separate accounts or tenants.
• Name the asset, the threat and the impact before recommending controls.
• Rank findings by real risk (likelihood and impact), with evidence for each.

Mistakes it avoids

• Treating a shared email, phone or address as proof when families and teams share them.
• Copying the profile's weights, thresholds or code as if they were validated for this data.
• Long lists of generic issues with no evidence they apply.
• Testing or scanning systems without permission.
• Presenting a legal opinion as final.

What a good result looks like

An example, not your task: The same customer appears in your shop, support tool and billing tool, and you want one shared record for each.

• Scope. The sources, the record type, the rule for "same" and the cost of a wrong merge.
• Sources and fields. Each source with its fields and which fields can be trusted.
• Match plan. For each field, how to clean it, how to compare it and how much weight it carries.
• Decision bands. Starting cut-offs for link, review and new, marked as figures to test.
• Merge and split process. Who proposes, who reviews, what is recorded and how to undo a merge.
• Consistency checks. Checks that two systems agree on who is who, and what a failure means.

How the work is checked

• Each finding has a location, evidence, severity and a specific fix.
• Likely false positives are checked and removed.
• Items that need a lawyer or auditor are clearly marked.
• 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.

Capabilities

• Fix the scope. It confirms the sources, the fields, what counts as the same and the cost of a wrong merge. Anything missing is asked once, then marked MISSING.
• Clean each field. It plans one form per field before comparing, such as lower-case emails, phone numbers in one format and nicknames matched to full names.
• Narrow the search. It picks match keys, such as email domain or phone start, so you compare likely pairs and not every pair.
• Score a pair. It compares field by field, with more weight on strong fields. A missing field never raises the score.
• Set the bands and the review. It sets cut-offs for link, review and new, and says who reviews and how a bad merge is split again.
• Check privacy and consistency. It plans checks that two systems give the same answer, and flags personal data to mask or leave out.

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 identity-graph-operator 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/identity-graph-operator/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 systems that hold the records, such as your shop, support tool, billing tool and email list.
• The fields each system holds, such as email, phone, name and address.
• What counts as the same, such as "same email means same customer", and what a wrong merge would cost.
• A small sample of records with personal details masked (optional)
• Where your customers live, and who can review unsure matches (optional)

Helpful context (optional)

• A masked export of 30 to 50 records from each system, including pairs you already know are the same person.
• A list of pairs that look the same but are not, such as two people at one shared email.
• The merges you made before that you later regretted.

Tools and access

• Claude, ChatGPT or Gemini with file upload: It reads a masked export from each system and tests your rules by hand on pairs you choose.

Functions and deliverables

• Identity graph designer: Get a plan to match duplicate customer, company or product records across your tools, with match keys, scoring, review steps and privacy checks.
  • A match plan with the fields to compare, how to clean each, and how heavily to weigh it
  • Three decision bands, which are link automatically, review, or treat as new
  • A review and undo process for merges and splits
  • Sample test pairs to try on your own data before you trust the rules
  • Privacy notes and a list of what is missing
  Source: Michael Sitarzewski and Agency Agents contributors.
• Screenshot privacy masker: Mask phone numbers, emails, keys and IDs in a screenshot on your own computer. Check the masked copy before you share it.
  • A masked copy of your image, saved as a new file
  • A list of what was found and where
  • A test report you can read before anything is changed
  • A short summary of how many regions were masked
  Source: sickn33.
• Customer record merger: Merge duplicate customers in Shopify. Give the record to keep and the one to merge in, check a preview, and keep tags and notes. Cannot be undone.
  • A preview of the merge, with both records side by side
  • One combined customer record when Shopify's own merge works
  • Tags and notes copied to the record you keep
  • A pointer note on the old record if the fallback runs
  • A summary of which path ran and any errors
  Source: 40rty AI.
• Shopify duplicate customer record finder: Find Shopify customer records with matching contact details or names. Get likely duplicate groups to review; records stay separate until you resolve them.
  • Groups of likely duplicate customers
  • Order counts and spend for each record
  • Plain or JSON output
  • A list ready to merge by hand
  Source: 40rty AI.
• me.md builder: Answer a few questions, or let your AI use what it remembers. Get one private file that tells any AI who you are.
  • One me.md file with up to ten dated sections
  • A list of facts taken from memory, for you to check
  • A short note of what it removed for privacy
  • The two next steps, to save it and set it up

Tasks you can use it for

• The same customer appears in your shop, support tool and billing tool, and you want one shared record for each.
• Two or more AI agents or automations touch the same customers or products and sometimes disagree about who is who.
• You are about to join two customer lists, for example after buying a business, and want rules for matching them.
• A merge went wrong once and you want a safer process with a review step and a way to undo it.

Limits to check before starting

• You only want likely duplicate customers found in one store list. Use the Shopify duplicate customer record finder.
• You want duplicates merged in your live store. Use the Customer record merger, and check each match first.
• You want AI agents to prove who they are. Use the AI agent identity designer.
• You want a messy spreadsheet cleaned. Use the Indian spreadsheet data cleaner.

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.

• Download the pack from this page and add it to your AI as a skill. If you cannot, paste SKILL.md into a new chat, then paste references/source-agent.md.
• List your systems and the fields in each. If you paste sample records, mask names, emails and phone numbers first.
• Type "Plan how to match these records. Show the evidence for each rule. Do not merge anything on a hunch. Mark every cut-off as a starting point to test."
• Read the three decision bands first. Move the cut-offs after you try the sample test pairs against your real records.
• Run the matching in your own tool or script. The AI plans the rules and does not run a matching engine.
• Have a person review every merge in the middle band, and keep a way to undo it.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/identity-graph-operator 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.