wehub-resource-sync
121 lines
4.9 KiB
Markdown
121 lines
4.9 KiB
Markdown
# Data Analyst Plugin
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A data analyst plugin primarily designed for [Cowork](https://claude.com/product/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.
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## Installation
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```
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claude plugins add knowledge-work-plugins/data
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```
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## What It Does
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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.
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### With a Data Warehouse Connection
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Connect your data warehouse MCP server (e.g., Snowflake, Databricks, BigQuery, or any SQL-compatible database) for the best experience. Claude will:
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- Query your data warehouse directly
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- Explore schemas and table metadata
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- Run analyses end-to-end without copy-pasting
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- Iterate on queries based on results
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### Without a Data Warehouse Connection
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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.
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## Commands
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| Command | Description |
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|---------|-------------|
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| `/analyze` | Answer data questions -- from quick lookups to full analyses |
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| `/explore-data` | Profile and explore a dataset to understand its shape, quality, and patterns |
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| `/write-query` | Write optimized SQL for your dialect with best practices |
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| `/create-viz` | Create publication-quality visualizations with Python |
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| `/build-dashboard` | Build interactive HTML dashboards with filters and charts |
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| `/validate` | QA an analysis before sharing -- methodology, accuracy, and bias checks |
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## Skills
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| Skill | Description |
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|-------|-------------|
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| `sql-queries` | SQL best practices across dialects, common patterns, and performance optimization |
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| `data-exploration` | Data profiling, quality assessment, and pattern discovery |
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| `data-visualization` | Chart selection, Python viz code patterns, and design principles |
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| `statistical-analysis` | Descriptive stats, trend analysis, outlier detection, and hypothesis testing |
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| `data-validation` | Pre-delivery QA, sanity checks, and documentation standards |
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| `interactive-dashboard-builder` | HTML/JS dashboard construction with Chart.js, filters, and styling |
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## Example Workflows
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### Ad-Hoc Analysis
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```
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You: /analyze What was our monthly revenue trend for the past 12 months, broken down by product line?
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Claude: [Writes SQL query] → [Executes against data warehouse] → [Generates trend chart]
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→ [Identifies key patterns: "Product line A grew 23% YoY while B was flat"]
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→ [Validates results with sanity checks]
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```
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### Data Exploration
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```
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You: /explore-data users table
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Claude: [Profiles table: 2.3M rows, 47 columns]
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→ [Reports: created_at has 0.2% nulls, email has 99.8% cardinality]
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→ [Flags: status column has unexpected value "UNKNOWN" in 340 rows]
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→ [Suggests: "High-value dimensions to explore: plan_type, signup_source, country"]
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```
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### Query Writing
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```
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You: /write-query I need a cohort retention analysis -- users grouped by signup month,
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showing what % are still active 1, 3, 6, and 12 months later. We use Snowflake.
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Claude: [Writes optimized Snowflake SQL with CTEs]
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→ [Adds comments explaining each step]
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→ [Includes performance notes about partition pruning]
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```
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### Dashboard Building
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```
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You: /build-dashboard Create a sales dashboard with monthly revenue, top products,
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and regional breakdown. Here's the data: [pastes CSV]
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Claude: [Generates self-contained HTML file]
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→ [Includes interactive Chart.js visualizations]
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→ [Adds dropdown filters for region and time period]
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→ [Opens in browser for review]
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```
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### Pre-Share Validation
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```
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You: /validate [shares analysis document]
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Claude: [Reviews methodology] → [Checks for survivorship bias in churn analysis]
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→ [Verifies aggregation logic] → [Flags: "Denominator excludes trial users
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which could overstate conversion rate by ~5pp"]
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→ [Confidence: "Ready to share with noted caveat"]
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```
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## Connecting Your Data Stack
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> If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](CONNECTORS.md).
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This plugin works best when connected to your data infrastructure. Add MCP servers for:
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- **Data Warehouse**: Snowflake, Databricks, BigQuery, Definite, or any SQL-compatible database
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- **Analytics/BI**: Amplitude, Looker, Tableau, or similar
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- **Notebooks**: Jupyter, Hex, or similar
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- **Spreadsheets**: Google Sheets, Excel
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- **Data Orchestration**: Airflow, dbt, Dagster, Prefect
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- **Data Ingestion**: Fivetran, Airbyte, Stitch
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Configure MCP servers in your `.mcp.json` or Claude Code settings to enable direct data access.
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