---
name: analyze
description: 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…
---

# analyze

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…

# analyzeby anthropic
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…

`npx skills add https://github.com/anthropics/knowledge-work-plugins --skill analyze`Download ZIPGitHub

## /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

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**Source**: https://github.com/anthropics/knowledge-work-plugins/tree/HEAD/data/skills/analyze
**Author**: anthropic
**Discovered via**: mcpservers.org