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. Configured for Snowflake, Amazon SageMaker, Amplitude, and Jira.

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 Snowflake Connected

Connect your Snowflake MCP server for the best experience. Claude will:

• Query Snowflake directly using your warehouse and role
• Explore schemas, databases, and table metadata
• Run analyses end-to-end without copy-pasting
• Iterate on queries based on live results

With SageMaker

Use Amazon SageMaker Studio notebooks for deeper exploration, ML workflows, and sharing analysis with your team. Claude can write Python/SQL code optimized for SageMaker notebooks, generate cells you can paste directly, and help structure notebook-based analyses.

Without a Data Warehouse Connection

Without a Snowflake connection, paste SQL results or upload CSV/Excel files for analysis and visualization. Claude can also write Snowflake 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
api-data-contracts	Generate typed data contracts from your OpenAPI spec for the UI agent — without Claude seeing real data

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"]

Your Data Stack

See CONNECTORS.md for the full list of connected tools.

This plugin is configured for your stack:

• Data Warehouse: Snowflake (connected via MCP)
• Notebooks: Amazon SageMaker Studio (use via AWS console)
• Product Analytics: Amplitude (connected via MCP)
• Project Tracker: Jira / Confluence via Atlassian (connected via MCP)
• Core Data API: OpenAPI/Swagger spec (Claude reads schema only — never sees real data)

Claude can query Snowflake directly, pull Amplitude insights, and reference Jira tickets. For SageMaker, Claude generates notebook-ready code you can run in Studio. For the core API, Claude generates typed data contracts from the OpenAPI spec that a separate UI agent uses to build data-driven interfaces.