MCPInstall

profiling-tables

by astronomer OfficialData & Databases

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.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/astronomer/agents --skill profiling-tables
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads profiling-tables automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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:

SELECT COLUMN_NAME, DATA_TYPE, COMMENT
FROM <database>.INFORMATION_SCHEMA.COLUMNS
WHERE TABLE_SCHEMA = '<schema>' AND TABLE_NAME = '<table>'
ORDER BY ORDINAL_POSITION

If the table name isn't fully qualified, search INFORMATION_SCHEMA.TABLES to locate it first.

Step 2: Size and Shape

Run via run_sql:

SELECT
    COUNT(*) as total_rows,
    COUNT(*) / 1000000.0 as millions_of_rows
FROM <table>

Step 3: Column-Level Statistics

For each column, gather appropriate statistics based on data type:

Numeric Columns

SELECT
    MIN(column_name) as min_val,
    MAX(column_name) as max_val,
    AVG(column_name) as avg_val,
    STDDEV(column_name) as std_dev,
    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY column_name) as median,
    SUM(CASE WHEN column_name IS NULL THEN 1 ELSE 0 END) as null_count,
    COUNT(DISTINCT column_name) as distinct_count
FROM <table>

String Columns

SELECT
    MIN(LEN(column_name)) as min_length,
    MAX(LEN(column_name)) as max_length,
    AVG(LEN(column_name)) as avg_length,
    SUM(CASE WHEN column_name IS NULL OR column_name = '' THEN 1 ELSE 0 END) as empty_count,
    COUNT(DISTINCT column_name) as distinct_count
FROM <table>

Date/Timestamp Columns

SELECT
    MIN(column_name) as earliest,
    MAX(column_name) as latest,
    DATEDIFF('day', MIN(column_name), MAX(column_name)) as date_range_days,
    SUM(CASE WHEN column_name IS NULL THEN 1 ELSE 0 END) as null_count
FROM <table>

Step 4: Cardinality Analysis

For columns that look like categorical/dimension keys:

SELECT
    column_name,
    COUNT(*) as frequency,
    ROUND(COUNT(*) * 100.0 / SUM(COUNT(*)) OVER(), 2) as percentage
FROM <table>
GROUP BY column_name
ORDER BY frequency DESC
LIMIT 20

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:

SELECT *
FROM <table>
LIMIT 10

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?
  • Are NULLs expected or problematic?

Uniqueness

  • Does the apparent primary key have duplicates?
  • Are there unexpected duplicate rows?

Freshness

  • When was data last updated? (MAX of timestamp columns)
  • Is the update frequency as expected?

Validity

  • Are there values outside expected ranges?
  • Are there invalid formats (dates, emails, etc.)?
  • Are there orphaned foreign keys?

Consistency

  • Do related columns make sense together?
  • Are there logical contradictions?

Step 7: Output Summary

Provide a structured profile:

Overview

2-3 sentences describing what this table contains, who uses it, and how fresh it is.

Schema

ColumnTypeNulls%DistinctDescription
...............

Key Statistics

  • Row count: X
  • Date range: Y to Z
  • Last updated: timestamp

Data Quality Score

  • Completeness: X/10
  • Uniqueness: X/10
  • Freshness: X/10
  • Overall: X/10

Potential Issues

List any data quality concerns discovered.

Recommended Queries

3-5 useful queries for common questions about this data.

FAQ

What does the profiling-tables skill do?

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.

How do I install profiling-tables?

Run npx skills add https://github.com/astronomer/agents --skill profiling-tables in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/profiling-tables/ (personal) or .claude/skills/profiling-tables/ (project).

Which AI agents can use profiling-tables?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is profiling-tables safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains profiling-tables?

It is published by astronomer in astronomer/agents, and it is an official skill from the maker of the technology it covers. It has 1,032 recorded installs.

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