Complexity level:
- Quick answer: Single metric, simple filter, factual lookup (e.g., "How many users signed up last week?")
- Full analysis: Multi-dimensional exploration, trend analysis, comparison (e.g., "What's driving the drop in conversion rate?")
- Formal report: Comprehensive investigation with methodology, caveats, and recommendations (e.g., "Prepare a quarterly business review of our subscription metrics")
Data requirements: Which tables, metrics, dimensions, and time ranges are needed
Output format: Number, table, chart, narrative, or combination

If a data warehouse MCP server is connected:

Explore the schema to find relevant tables and columns
Write SQL query(ies) to extract the needed data
Execute the query and retrieve results
If the query fails, debug and retry (check column names, table references, syntax for the specific dialect)
If results look unexpected, run sanity checks before proceeding

If no data warehouse is connected:

Ask the user to provide data in one of these ways:
- Paste query results directly
- Upload a CSV or Excel file
- Describe the schema so you can write queries for them to run
If writing queries for manual execution, use the sql-queries skill for dialect-specific best practices
Once data is provided, proceed with analysis

Before sharing results, run through validation checks:

If any check raises concerns, investigate and note caveats.

For quick answers:

For full analyses:

For formal reports:

When a chart would communicate results more effectively than a table:

Examples

Quick answer:

/analyze How many new users signed up in December?

Full analysis:

/analyze What's causing the increase in support ticket volume over the past 3 months? Break down by category and priority.

Formal report:

/analyze Prepare a data quality assessment of our customer table -- completeness, consistency, and any issues we should address.

Tips

Be specific about time ranges, segments, or metrics when possible
If you know the table names, mention them to speed up the process
For complex questions, Claude may break them into multiple queries
Results are always validated before presentation -- if something looks off, Claude will flag it