Data Plugin

Last verified: 13 February 2026 | Applies to: Pro, Max, Team, Enterprise

In 30 seconds

The Data plugin gives Claude SQL fluency across dialects, the ability to create interactive visualisations and dashboards, and statistical analysis capabilities. Point it at a database, a CSV, or a spreadsheet and ask questions in plain English — Claude writes the query, runs it, and presents results visually. It’s the analyst you don’t have to hire.

Key commands

No dedicated slash commands — the Data plugin activates when you work with data in Cowork. It enhances Claude’s native data handling with SQL capabilities, richer visualisations, and statistical tools.

What it knows

The Data plugin’s skills cover:

• SQL across dialects — PostgreSQL, MySQL, SQLite, BigQuery, Snowflake, and more. Specify your dialect and Claude writes correct syntax.
• Data visualisation — bar charts, line charts, pie charts, scatter plots, heatmaps. Generated as artifacts or as interactive HTML.
• Interactive dashboards — multi-chart HTML dashboards you can open in a browser.
• Statistical analysis — descriptive statistics, correlation, regression, significance testing.
• Data cleaning — deduplication, normalisation, type conversion, missing value handling.

How operators use it

Explore a dataset:

Here's our sales data for the last 12 months [upload CSV]. Give me a summary: total revenue, average order value, top 10 customers, and monthly trend. Include a line chart.

Query a database:

Connect to our PostgreSQL database. Show me all customers who haven't ordered in 90 days but had more than $5,000 in purchases last year. Export as a CSV.

Build a dashboard:

Create an interactive dashboard from this data. Include: revenue over time (line), revenue by region (bar), product breakdown (pie), and KPI cards for total revenue, AOV, and customer count.

Claude generates an HTML file you open in your browser — interactive filters, hover tooltips, and responsive layout.

Statistical analysis:

Is there a correlation between marketing spend and revenue by region? Run a correlation analysis and tell me if it's statistically significant.

Data cleaning:

Clean this customer data: remove duplicates (match on email), standardise phone numbers to E.164 format, fill missing country codes from postcode where possible, and split into separate files by region.

The honest take

What works well

• SQL generation is strong across dialects. Claude rarely gets syntax wrong if you specify the dialect.
• Dashboards are surprisingly good for a 20-minute effort. They’re not Tableau, but they’re functional and shareable.
• Plain-English querying is genuinely useful for operators who don’t know SQL but need answers from their data.

What doesn’t work as well

• Very large datasets (100,000+ rows) may need to be sampled or filtered before Claude can process them meaningfully.
• Statistical analysis should be verified. Claude can overstate significance or misapply methods.
• Interactive dashboards are static snapshots — they don’t auto-update when your data changes.

Who it’s best for: Operations and analytics leads who need quick answers from data without waiting for a data team. Small businesses without dedicated analysts. Anyone who has CSV files full of data and questions.

Related

• Data Analysis — full workflow guide
• Connecting Your Tools — connect databases and data sources
• All Plugins
• Plugin Commands Reference

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