Agent skill · data analytics · anthropics
explore-data
Profile and explore a dataset to understand its shape, quality, and patterns. Use when encountering a new table or file, checking null rates and column distributions, spotting data quality issues like duplicates or suspicious values, or deciding which dimensions and metrics to analyze.
Why this skill is useful
Provides specific profiling queries and checks for assessing data quality that the AI wouldn't reliably generate on its own.
What it needs
About 7k tokens when loaded. Last updated 2026-08-07. 23,345 stars on the source repository.
What this skill does
/explore-data - Profile and Explore a Dataset If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md. Generate a comprehensive data profile for a table or uploaded file. Understand its shape, quality, and patterns before diving into analysis. Usage Workflow 1. Access the Data If a data warehouse MCP server is connected: 1. Resolve the table name (handle schema prefixes, suggest matches if ambiguous) 2. Query table metadata: column names, types, descriptions if available 3. Run profiling queries against the live data If a file is provided (CSV, Excel, Parquet, JSON): 1. Read the file and load into a working dataset 2. Infer column types from the data If neither: 1. Ask the user to provide a table name (with their warehouse connected) or upload a file 2. If they describe a table schema, provide guidance on what profiling queries to run 2. Understand Structure Before analyzing any data, understand its structure: Table-level questions: How many rows and columns? What is the grain (one row per what)? What is the primary key? Is it unique? When was the data last updated? How far back does the data go? Column classification — categorize each column as one of: Identifier: Unique keys, foreign keys, entity IDs Dimension: Categorical attributes for grouping/filtering (status, type, region, category) Metric: Quantitative values for measurement (revenue, count, duration, score) Temporal: Dates and timestamps (createdat, updatedat, eventdate) Text: Free-form text fields (description, notes, name) Boolean: True/false flags Structural: JSON, arrays, nested structures 3. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills anthropics/explore-data