Profiling Tables Agent Skill

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.

964 tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

416

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

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

The instruction itself

22 sections, as written by the author

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

|--------|------|--------|----------|-------------|

| ... | ... | ... | ... | ... |

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.

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How to use it

Copy the folder

Take astronomer/profiling-tables from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.