analyze

SKILL.md

/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