Hire an AI GIS quality checker

Everything it can hand back

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

• A verdict of pass, conditional pass or fail, with counts of critical, major and minor issues
• Each problem with its place, such as feature ID, the likely cause and a fix
• The checks it could not run, as commands or steps for QGIS, GDAL or PostGIS
• A metadata review that lists what is missing, such as source date, accuracy or contact
• A list of what to retest after the fixes

How it does the job well

• Evidence for every finding: a feature ID, a value or a location. A hunch goes under TO CHECK, not SEEN.
• Do not say a dataset passes, or that shapes are valid, unless the check was run and the user pasted the result.
• Do not name a tool menu, command option or standard clause unless you are sure. If unsure, write CHECK: and name the document to look up.
• Do not edit, delete or publish the data. Hand over the report and let the user fix and release.
• 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.

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 new dataset arrives from a vendor or another team, and you want to check it before you use it.

• Dataset. What was reviewed, what it is for, the declared coordinate system and what is missing.
• Verdict. Pass, conditional pass or fail, with issue counts by severity.
• Findings. Each problem with severity, place, evidence, likely cause, fix and whether it was seen or still to check.
• Checks not run. Shape and map checks for QGIS, GDAL or PostGIS, with what a pass looks like.
• Metadata review. Which metadata items are present, partial or missing.
• Retest after fixes. The checks to repeat before the data ships.

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

• Check the intake. It checks the stated coordinate system against sample values, the schema against the spec, and counts and extent against what you expect.
• Look at attributes. It looks for blanks, wrong types, values outside allowed lists and duplicate records in your sample.
• Plan the shape checks. For gaps, overlaps, self-crossing shapes and broken lines it writes the exact test to run on the full file, since a sample cannot show them.
• Review the metadata. It lists what is missing, such as lineage, accuracy, contact, use limits, datum and dates.
• Rate and locate. Each finding gets a severity, a feature ID or place, a likely cause and a fix.
• Give the verdict. It gives pass, conditional pass or fail, and names which checks still need to run before a pass can stand.

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 gis-qa-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/gis-qa-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

• The dataset's name, format and what it is for, such as parcels in a shapefile for a public map
• The schema or field list, with a sample of rows (about 20, copied from the attribute table)
• The coordinate system the data says it uses, and the area it should cover
• The spec or rules it must meet, such as required fields and allowed values
• Metadata, row count you expect and control points for an accuracy check (optional)

Helpful context (optional)

• The output of ogrinfo for the layer.
• A few rows that you already know are wrong.
• The data provider's spec or documentation.

Tools and access

• QGIS: Free desktop map software. Its topology checker finds gaps, overlaps and bad shapes.
• GDAL ogrinfo: A free command line tool that prints a file's coordinate system, fields and row count so you can paste them in.
• Claude Code or another AI that can run code on your files: It can run the checks itself. In a plain chat you run them and paste the results.

Functions and deliverables

• GIS quality checker: Check a map dataset before release. Get a pass or fail verdict, each problem with its location and likely cause, and checks to run in QGIS.
  • A verdict of pass, conditional pass or fail, with counts of critical, major and minor issues
  • Each problem with its place, such as feature ID, the likely cause and a fix
  • The checks it could not run, as commands or steps for QGIS, GDAL or PostGIS
  • A metadata review that lists what is missing, such as source date, accuracy or contact
  • A list of what to retest after the fixes
  Source: Michael Sitarzewski and Agency Agents contributors.
• Tabular dataset explorer: Share a table or data file. Profile columns, missing values, distributions, links and unusual rows, then report findings and next checks.
  • Size, types and sample rows
  • A missing data and duplicate report
  • Charts of each column's spread
  • Strong links between columns
  • Outliers and a short findings summary
  Source: Burhan Sebin.
• Dataset cleaner and validator: Give it a dataset and expected schema. Get a quality profile, proposed corrections, validation results and a change log.
  • Cleaned dataset file
  • Log of every transformation
  • Data quality report
  • List of remaining issues
  Source: Burhan Sebin.
• Data quality auditor: Share data and quality rules. Get missing values, duplicates and unusual records, with ranked fixes and a score where supported.
  • Data quality score
  • Missing value and outlier findings
  • Ranked repair plan
  • Monitoring checklist
  Source: Alireza Rezvani.
• 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 new dataset arrives from a vendor or another team, and you want to check it before you use it.
• You are about to publish a map layer or web service and need a go or no-go.
• Two layers do not line up, and you suspect a coordinate system mismatch.
• A grant, regulator or client asks for metadata and accuracy notes with the data.

Limits to check before starting

• You need a map made or analysis run. Use the GIS analyst.
• You need data cleaned, converted or moved into a pipeline. Use the Spatial Data Engineer agent.
• You need a survey-grade accuracy sign-off. Use a licensed surveyor or your data provider's validation process.
• You want it to open and test your files itself. A chat AI can't. Use QGIS or GDAL for the checks it lists, or an AI that can run code on your files.

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. Or paste SKILL.md into a new chat, then paste references/source-agent.md after it.
• Paste the schema, the sample rows, the stated coordinate system, the spec and what the data is for. For a big file, export the field list and 20 rows, not the whole table.
• Read the report. Anything marked "seen" came from your paste. Anything marked "to check" needs you to run the check on the full dataset.
• Run the listed checks in QGIS, GDAL or PostGIS, and paste the results back. Ask the agent to update the verdict.
• Fix the critical and major issues at the source, then run the same checks again. A fix only counts when the check passes.
• Add the report and the metadata notes to the dataset's record before you release 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/gis-qa-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.