Hire an AI data visualization engineer

Everything it can hand back

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

• The chart type to use, and why it fits your question
• A setup for each axis and mark, such as what goes on the x axis, the baseline and the sort order
• A colour plan that works for colour-blind readers and in black and white
• A title that states the takeaway, plus labels and units
• An honesty check with pass or fail for baseline, axes, size, colour and clutter
• Accessibility notes and a build note for your tool

How it does the job well

• Never claim you ran a colour-blind simulator, a speed test or a reader test. List them under open checks.
• Do not invent numbers or example data. Use the user's columns.
• The question picks the chart, not the aesthetics. Comparison → bars; trend over time → line; distribution → histogram/box/violin; correlation → scatter; part-to-whole → stacked bar or (rarely) pie for 2-3 slices. Starting from "let's make it a donut" is how charts lie.
• Encode quantities in position and length, not angle or area. Human perception ranks position > length > angle > area > color for reading numbers. That's why bars beat pies and why a bubble chart's sizes are always misjudged. Choose the channel by decoding accuracy.
• Never truncate a bar chart's baseline; be deliberate about line-chart axes. Bars encode value by length, so they must start at zero — a truncated bar baseline is a visual lie. Line charts can use a non-zero baseline to show change, but only when labeled and honest about it.
• Ban the dual-axis-two-series trick unless you can defend it. Two y-axes let you slide the scales to manufacture any correlation you want. Prefer indexed values, small multiples, or a connected scatter. If you must dual-axis, make the reader aware.
• Agree the question and the decision it supports before analysing.

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: You have a table and a question, and you do not know whether to use bars, lines or something else.

• Chart plan. The chart type, the axis setup, the colour plan and a title that states the takeaway.
• Honesty check. Pass or fail for baseline, dual axes, frame shape, hidden spread, labels and clutter.
• Accessibility. A colour-blind check, a text summary for screen readers and a data table fallback.
• Build note. How to set the chart up in your tool, and a warning if your row count will slow it down.
• Open checks. What to test with a real reader or your real data before you share the chart.

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

• Start from the question. It asks what decision the chart serves, and whether the answer is a comparison, trend, spread, link between two things or parts of a whole.
• Read the data shape. It looks at your column types, how many categories you have and how many rows, which rules some charts out.
• Pick the chart. It chooses the chart type that the eye reads most accurately for that question, and says what to avoid.
• Set axes and colour. It sets baselines, sort order and a colour scale that suits the data, and never lets colour alone carry meaning.
• Run the honesty check. It checks baseline, dual axes, shape of the frame, averages that hide spread, labels and clutter.
• Plan for the real data size. It says when the chart will slow down or blur, and when to group or sample the data first.

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-data-visualization-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-data-visualization-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 question the chart must answer, in one sentence
• A list of your columns, what each holds, and how many rows you have
• Where the chart will appear, such as a slide, a web page or a printed report
• An existing chart to review, as an image or a description (optional)
• The tool you will use, such as Excel, Google Sheets, Tableau, Power BI or Python (optional)
• Who reads it and how much time they have (optional)

Helpful context (optional)

• A screenshot of the chart you want reviewed.
• Your brand colours, so the palette keeps them where they pass the colour-blind check.
• The one number or fact your reader must remember.

Tools and access

• Claude, ChatGPT or Gemini: They can read a screenshot of a chart you already made and mark what misleads.
• Excel, Google Sheets, Tableau, Power BI or Python: You build the chart there from the plan. The agent does not run your tools.

Functions and deliverables

• Data visualization engineer agent: Pick the right chart for your data and check it does not mislead. Get a plan with axes, colours and a pass or fail check.
  • The chart type to use, and why it fits your question
  • A setup for each axis and mark, such as what goes on the x axis, the baseline and the sort order
  • A colour plan that works for colour-blind readers and in black and white
  • A title that states the takeaway, plus labels and units
  • An honesty check with pass or fail for baseline, axes, size, colour and clutter
  • Accessibility notes and a build note for your tool
  Source: Michael Sitarzewski and Agency Agents contributors.
• Data chart maker: Turn your data into a clean chart. Give it a file or pasted rows and get a chart image or interactive plot, with the right chart type picked for you.
  • Chart image or interactive plot
  • Chart type recommendation
  • The code that made it
  • Edits on request
  Source: Anthropic.
• Shopify test setup picker: Find out what you can A/B test on your Shopify plan, which testing route fits, and what to ask tools about flicker and stats. Get a setup score.
  • What is testable on your plan and what is not
  • A route for each idea: browser-based, or server-side and theme tests
  • Flicker, speed and stats engine notes, and a list of questions to ask every tool
  • A setup score out of 100, with the sum shown
• Class discussion format picker: Pick the right class discussion format for your topic and group. Get a plan with setup, steps, sentence starters and fixes for when talk stalls.
  • A recommended discussion format
  • Steps to run it
  • Sentence starters for students
  • Moves for when talk stalls
  • Ways to include quiet students
  Source: Gareth Manning.
• 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

• You have a table and a question, and you do not know whether to use bars, lines or something else.
• A chart for your board pack, report or slide looks cluttered or hard to read.
• A chart you made may be misleading, such as a bar chart that does not start at zero.
• Your chart must work for colour-blind readers, on a screen reader or in a black and white print.

Limits to check before starting

• You want the chart drawn from your data now: use the Data chart maker or the Chart maker.
• You want a dashboard file people can open offline: use the Offline dashboard builder.
• You want an existing PowerPoint chart rebuilt so you can edit it: use the PowerPoint editable chart rebuilder.
• You want a report that explains what your numbers say in words: use the Analytics report writer.

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.
• Write your question in one sentence. List your columns, what each holds and the number of rows. If you have a chart already, attach its image.
• Say where the chart will be used and which tool you will build it in.
• Read the honesty check first. Fix any failed line before you polish anything else.
• Build the chart in your tool from the plan. If you want code, ask for D3, Vega-Lite, Plotly or Python code, and run it yourself.
• Show the chart to someone new for three seconds. If they cannot say the takeaway, ask the agent for a simpler version.

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-data-visualization-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.