/analyze - Answer Data Questions

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

Answer a data question, from a quick lookup to a full analysis to a formal report.

Usage

/analyze <natural language question>

Workflow

1. Understand the Question

Parse the user's question and determine:

• 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

2. Gather Data

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

3. Analyze

• Calculate relevant metrics, aggregations, and comparisons
• Identify patterns, trends, outliers, and anomalies
• Compare across dimensions (time periods, segments, categories)
• For complex analyses, break the problem into sub-questions and address each

4. Validate Before Presenting

Before sharing results, run through validation checks:

• Row count sanity: Does the number of records make sense?
• Null check: Are there unexpected nulls that could skew results?
• Magnitude check: Are the numbers in a reasonable range?
• Trend continuity: Do time series have unexpected gaps?
• Aggregation logic: Do subtotals sum to totals correctly?

If any check raises concerns, investigate and note caveats.

5. Present Findings

For quick answers:

• State the answer directly with relevant context
• Include the query used (collapsed or in a code block) for reproducibility

For full analyses:

• Lead with the key finding or insight
• Support with data tables and/or visualizations
• Note methodology and any caveats
• Suggest follow-up questions

For formal reports:

• Executive summary with key takeaways
• Methodology section explaining approach and data sources
• Detailed findings with supporting evidence
• Caveats, limitations, and data quality notes
• Recommendations and suggested next steps

6. Visualize Where Helpful

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

• Use the data-visualization skill to select the right chart type
• Generate a Python visualization or build it into an HTML dashboard
• Follow visualization best practices for clarity and accuracy

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