data

Data Analyst Plugin

A data analyst plugin primarily designed for Cowork, Anthropic's agentic desktop application — though it also works in Claude Code. SQL queries, data exploration, visualization, dashboards, and insight generation. Works with any data warehouse, any SQL dialect, and any analytics stack.

Installation

claude plugins add knowledge-work-plugins/data

What It Does

This plugin transforms Claude into a data analyst collaborator. It helps you explore datasets, write optimized SQL, build visualizations, create interactive dashboards, and validate analyses before sharing with stakeholders.

With a Data Warehouse Connection

Connect your data warehouse MCP server (e.g., Snowflake, Databricks, BigQuery, or any SQL-compatible database) for the best experience. Claude will:

• Query your data warehouse directly
• Explore schemas and table metadata
• Run analyses end-to-end without copy-pasting
• Iterate on queries based on results

Without a Data Warehouse Connection

Without a data warehouse connection, paste SQL results or upload CSV/Excel files for analysis and visualization. Claude can also write SQL queries for you to run manually, and then analyze the results you provide.

Commands

Command	Description
/analyze	Answer data questions -- from quick lookups to full analyses
/explore-data	Profile and explore a dataset to understand its shape, quality, and patterns
/write-query	Write optimized SQL for your dialect with best practices
/create-viz	Create publication-quality visualizations with Python
/build-dashboard	Build interactive HTML dashboards with filters and charts
/validate	QA an analysis before sharing -- methodology, accuracy, and bias checks

Skills

Skill	Description
sql-queries	SQL best practices across dialects, common patterns, and performance optimization
data-exploration	Data profiling, quality assessment, and pattern discovery
data-visualization	Chart selection, Python viz code patterns, and design principles
statistical-analysis	Descriptive stats, trend analysis, outlier detection, and hypothesis testing
data-validation	Pre-delivery QA, sanity checks, and documentation standards
interactive-dashboard-builder	HTML/JS dashboard construction with Chart.js, filters, and styling

Example Workflows

Ad-Hoc Analysis

You: /analyze What was our monthly revenue trend for the past 12 months, broken down by product line?

Claude: [Writes SQL query] → [Executes against data warehouse] → [Generates trend chart]
       → [Identifies key patterns: "Product line A grew 23% YoY while B was flat"]
       → [Validates results with sanity checks]

Data Exploration

You: /explore-data users table

Claude: [Profiles table: 2.3M rows, 47 columns]
       → [Reports: created_at has 0.2% nulls, email has 99.8% cardinality]
       → [Flags: status column has unexpected value "UNKNOWN" in 340 rows]
       → [Suggests: "High-value dimensions to explore: plan_type, signup_source, country"]

Query Writing

You: /write-query I need a cohort retention analysis -- users grouped by signup month,
     showing what % are still active 1, 3, 6, and 12 months later. We use Snowflake.

Claude: [Writes optimized Snowflake SQL with CTEs]
       → [Adds comments explaining each step]
       → [Includes performance notes about partition pruning]

Dashboard Building

You: /build-dashboard Create a sales dashboard with monthly revenue, top products,
     and regional breakdown. Here's the data: [pastes CSV]

Claude: [Generates self-contained HTML file]
       → [Includes interactive Chart.js visualizations]
       → [Adds dropdown filters for region and time period]
       → [Opens in browser for review]

Pre-Share Validation

You: /validate [shares analysis document]

Claude: [Reviews methodology] → [Checks for survivorship bias in churn analysis]
       → [Verifies aggregation logic] → [Flags: "Denominator excludes trial users
          which could overstate conversion rate by ~5pp"]
       → [Confidence: "Ready to share with noted caveat"]

Connecting Your Data Stack

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.

This plugin works best when connected to your data infrastructure. Add MCP servers for:

• Data Warehouse: Snowflake, Databricks, BigQuery, or any SQL-compatible database
• Analytics/BI: Amplitude, Looker, Tableau, or similar
• Notebooks: Jupyter, Hex, or similar
• Spreadsheets: Google Sheets, Excel
• Data Orchestration: Airflow, dbt, Dagster, Prefect
• Data Ingestion: Fivetran, Airbyte, Stitch

Configure MCP servers in your .mcp.json or Claude Code settings to enable direct data access.