How can I profile just one specific column?

You can use the --field argument followed by the column name, for example: /dr-profile --field amount.

What does the outlier detection flag?

The skill flags values that are more than three standard deviations (3σ) from the mean, helping you identify potentially erroneous or unusual financial records.

What should I do if the profiling tool reports high cardinality?

High cardinality in a categorical field suggests a high ratio of unique values to rows. The skill will recommend investigating if that field should be moved to a separate dimension table for better data structure.

What metrics does the profiling tool provide for numeric fields?

For numeric fields, the skill provides min, max, mean, median, standard deviation, variance, and percentiles (P1 through P99), along with a count of statistical outliers.

Datarails Table Profiling

Name: Datarails Table Profiling
Author: Datarails

byDatarails

0•

Data Science & ML

Profiles Datarails Finance OS table fields to analyze numeric statistics, categorical distributions, and data quality metrics.

The Datarails Table Profiling skill provides deep insights into Finance OS datasets by automating the analysis of column-level data. It calculates comprehensive statistics for numeric fields—including mean, median, and 3σ outlier detection—while evaluating categorical fields for cardinality and frequency distributions. This tool is indispensable for data analysts and financial controllers who need to audit data quality, identify anomalies, and understand the shape of their financial data before performing complex queries or reporting.

Key Features

01Categorical distribution analysis and cardinality tracking

02Automated numeric profiling with mean, median, and percentile calculations

03Data quality indicators flagging null rates and normalization needs

04Granular field-level deep dives for specific column investigations

050 GitHub stars

06Statistical outlier detection using the 3σ (three-sigma) method

Use Cases

01Verifying data completeness and null rates before building financial models

02Auditing financial transactions for statistical anomalies or unexpected outliers

03Identifying high-cardinality fields that may require database normalization

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add datarails/dr-claude-code-plugins-re profile

For use in Claude.ai and ChatGPT

Key Features

01Categorical distribution analysis and cardinality tracking

02Automated numeric profiling with mean, median, and percentile calculations

03Data quality indicators flagging null rates and normalization needs

04Granular field-level deep dives for specific column investigations

050 GitHub stars

06Statistical outlier detection using the 3σ (three-sigma) method

Use Cases

01Verifying data completeness and null rates before building financial models

02Auditing financial transactions for statistical anomalies or unexpected outliers

03Identifying high-cardinality fields that may require database normalization

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add datarails/dr-claude-code-plugins-re profile

For use in Claude.ai and ChatGPT