Agent skill · data analytics · anthropics
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 segments over time, or preparing a formal data report for stakeholders.
Why this skill is useful
Provides a structured approach to data analysis, including SQL query generation and validation checks that the AI wouldn't reliably generate on its own.
What it needs
About 3k tokens when loaded. Last updated 2026-08-07. 23,345 stars on the source repository.
What this skill does
/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 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: 1. Explore the schema to find relevant tables and columns 2. Write SQL query(ies) to extract the needed data 3. Execute the query and retrieve results 4. If the query fails, debug and retry (check column names, table references, syntax for the specific dialect) 5. If results look unexpected, run sanity checks before proceeding If no data warehouse is connected: 1. 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 2. If writing queries for manual execution, use the sql-queries skill for dialect-specific best practices 3. 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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills anthropics/analyze