Hire an AI data repair engineer

Everything it can hand back

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

• A list of pattern families, each with a name, example rows and a rough count
• One fix rule per family, as an Excel formula, SQL line or one-line Python function
• A before and after table that shows each rule working on your sample
• A review list of rows and families the agent will not auto-fix, with the reason
• A change log layout and a row count check, so no row goes missing

How it does the job well

• You write the rule. The user runs it. Never claim you changed their data.
• Do not state a count, saving or rate you did not work out from the user's data.
• Refer legal or privacy questions about the data to a qualified professional.
• Agree the question and the decision it supports before analysing.
• Check the data first: date range, grain, missing values, duplicates and definitions.
• Compare like with like: same period length, definitions and segments.
• Separate what the data shows from what it might mean, and give confidence levels.

Mistakes it avoids

• Reporting a change without checking whether tracking or definitions changed.
• Treating correlation as cause.
• Averages that hide important segments or outliers.

What a good result looks like

An example, not your task: A column holds the same thing written many ways, such as dates, phone numbers, country names or currency.

• Pattern families. Your broken rows sorted into a few groups, each with examples.
• Fix rules. One short formula, SQL line or function per family, with the assumptions behind it.
• Before and after. Each rule run on your sample rows, so you can see it work or fail.
• Review list. Rows and families a person must check, with the reason for each.
• Change log and count check. The columns to record for every change, and the check that no row went missing.
• Open checks. What to confirm on your real data before you run the rules for real.

How the work is checked

• Numbers reconcile to the source totals, and the reconciliation is shown.
• Every chart and number states its source, period and definition.
• Limits and next checks are listed.
• 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

• Set the target. It confirms the column, the format you want and the row key, and asks once for what is missing.
• Group the broken rows. It sorts your sample into pattern families by the shape of the error, so one rule can cover many rows.
• Write one rule per family. It writes a single short formula or function for each family. It never writes code that imports, runs commands or reads files.
• Test on the sample. It runs each rule on your sample rows and shows before and after. A rule that breaks any row is rejected.
• Set rows aside. Rows it cannot fix with confidence, and rows with different keys that look alike, go to a review list. It never merges two records.
• Prove nothing was lost. It sets out the change log columns and the check that source rows equal fixed rows plus review rows.

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 engineering-ai-data-remediation-engineer 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/engineering-ai-data-remediation-engineer/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

• What the table is, and what the column in question should hold
• 20 to 50 sample rows that show the broken values, with private details swapped for made-up ones
• How many bad rows there are in total, if you know
• The format you want, such as dates as YYYY-MM-DD
• The column that uniquely names each row, such as an order number
• Rules you already apply, so the agent skips what is fixed (optional)

Helpful context (optional)

• A list of the valid values for the column, such as the country names you accept.
• Rows that you fixed by hand before, with the answer you chose.
• A note on which fields are private, so they stay out of the chat.

Tools and access

• Claude, ChatGPT or Gemini: A longer chat lets it keep your target format and key in view while it tests each rule.
• Excel, Google Sheets, SQL or Python: These run the rules on the full file. The chat only tests them on your sample.

Functions and deliverables

• AI data repair engineer: Fix messy data with a few tested rules, not row-by-row. You get fix formulas, a change log and a review list of risky rows.
  • A list of pattern families, each with a name, example rows and a rough count
  • One fix rule per family, as an Excel formula, SQL line or one-line Python function
  • A before and after table that shows each rule working on your sample
  • A review list of rows and families the agent will not auto-fix, with the reason
  • A change log layout and a row count check, so no row goes missing
  Source: Michael Sitarzewski and Agency Agents contributors.
• Spreadsheet formula auditor: Give it a spreadsheet. Get formula errors, broken links and model checks such as balance sheet tie-out listed.
  • A list of formula errors by cell
  • Broken link and reference flags
  • Balance sheet and cash tie-out results
  • Unit and fiscal period warnings
  Source: nighomni123.
• Software feature repair engineer: Name the feature and share the code. Get dependency tracing, focused fixes and a report of checks and open gaps.
  • A map of the feature's files and what it connects to
  • A diagnosis with a risk label on each issue, written before any change
  • Fixes made one at a time, each checked with a test
  • Test results for the feature and for the code that uses it
  • A list of every change made
  Source: Alireza Rezvani.
• Excel workbook change reviewer: Share old and new workbooks. Get changed values, formulas and file features explained.
  • workbook-change-review.xlsx and change-register.md.
  • A record of source files, changes and check results
  • Missing facts and any checks that could not run
• Metric definition writer: Share a metric, calculation and business use. Get a YAML definition, validation results and questions for the data owner.
  • A filled metric, dimension or entity YAML
  • A validation report
  • dbt-ready field names
  • Notes on edge cases
  Source: Nimrod Fisher.

Tasks you can use it for

• A column holds the same thing written many ways, such as dates, phone numbers, country names or currency.
• An import left thousands of rows with errors, and fixing them by hand would take days.
• You need a record of every change, so you can explain to a boss or an auditor what was altered.
• You must clean data you cannot send to a cloud service, and want fix rules you can run on your own machine.

Limits to check before starting

• You only want to know what is wrong with a file, not to fix it: use the Dataset profiler or the Data quality scorecard maker.
• You want a file cleaned over several passes until it passes your checks: use the Spreadsheet and CSV cleaner.
• You need to design a data pipeline, warehouse or table layout: use the Data pipeline designer.
• You want to apply fixes straight to live data. Run every rule on a copy first, and keep a backup.

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. Or open a new chat and paste SKILL.md, then references/source-agent.md.
• Take 20 to 50 broken rows from your data. Replace names, emails and ID numbers with made-up values that keep the same shape.
• Paste them with the column name, the target format and the row key. Say which tool you will run the rules in, such as Excel, SQL or Python.
• Read the families first. If two families look like one, or one looks like two, say so and ask it to regroup.
• Run each rule on a copy of your data. Count the rows before and after, and check that fixed rows plus review rows equal your starting count.
• Spot check 10 fixed rows against the original. Only then apply the rule to your live data.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/engineering-ai-data-remediation-engineer 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.