# profiling-tables

> Claude/Codex skill by [@astronomer](https://omgskills.com/library/astronomer/index.md) · 454 stars · Updated 2026-10-14

Deep-dive data profiling for a specific table. Use when the user asks to profile a table, wants statistics about a dataset, asks about data quality, or needs to understand a table's structure and content. Requires a table name.

Tags: agentic-workflow, agents, ai, ai-agents, airflow, apache-airflow, claude, cursor, dag, data-engineering, data-pipelines, dbt, llm, mcp, orchestrator, skills, workflow-automation, workflow-management, workflow-orchestration, workflows

## Install

```sh
git clone https://github.com/astronomer/agents /tmp/agents && ln -s /tmp/agents/skills/profiling-tables ~/.claude/skills/profiling-tables
```

## From README

Data Profile Generate a comprehensive profile of a table that a new team member could use to understand the data. Step 1: Basic Metadata Query column metadata: If the table name isn't fully qualified, search INFORMATIONSCHEMA.TABLES to locate it first. Step 2: Size and Shape Run via runsql: Step 3: Column-Level Statistics For each column, gather appropriate statistics based on data type: Numeric Columns String Columns Date/Timestamp Columns Step 4: Cardinality Analysis For columns that look like categorical/dimension keys: This reveals: High-cardinality columns (likely IDs or unique values) Low-cardinality columns (likely categories or status fields) Skewed distributions (one value dominates) Step 5: Sample Data Get representative rows: If the table is large and you want variety, sample from different time periods or categories. Step 6: Data Quality Assessment Summarize quality across dimensions: Completeness Which columns have NULLs? What percentage?

## Links

- HTML page: https://omgskills.com/skills/astronomer/agents/profiling-tables/
- GitHub: https://github.com/astronomer/agents
- Author: [@astronomer](https://omgskills.com/library/astronomer/index.md)

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