/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-queriesskill 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-visualizationskill 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