Answer data questions -- from quick lookups to full analyses. Use when looking up a single metric, investigating what's driving a trend or drop, comparing segments over time, or preparing a formal data report for stakeholders.
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
