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:
tmorrowdev