skills.rest
Datarails
DatarailsOFFICIAL
Explore all skills by this author →
WebsiteGitHub

dr-profile

Profile Datarails Finance OS tables with statistical summaries and data quality assessments.

3|3|Updated Feb 1, 2026
Search Tags:#data quality#data profiling#table analysis#datarails#numeric fields#categorical fields
One-click install
npx skills add https://github.com/Datarails/dr-claude-code-plugins-re --skill dr-profile
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dr-profile
Source: https://github.com/Datarails/dr-claude-code-plugins-re/tree/main/skills/profile
Command: npx skills add https://github.com/Datarails/dr-claude-code-plugins-re --skill dr-profile

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides in-depth analysis of Datarails Finance OS tables, helping users understand data distributions, identify statistical anomalies, and assess data quality for numeric and categorical fields.

Core Features & Use Cases

  • Comprehensive Table Profiling: Generates detailed statistics for numeric fields (min, max, mean, std dev, outliers) and categorical fields (cardinality, top values, null counts).
  • Targeted Analysis: Allows users to focus profiling on specific fields or data types (numeric/categorical only).
  • Use Case: Before running anomaly detection, use /dr-profile on your 'GL Transactions' table to understand the typical range of 'amount' and identify any unusually high or low values that might skew the anomaly detection results.

Quick Start

Profile the Datarails table with ID '11442' to get a full overview of its numeric and categorical fields.

Frequently Asked Questions about dr-profile

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I profile Datarails Finance OS tables for statistical insights?▼

You profile Datarails tables by summarizing numeric fields for metrics like min, max, mean, and outliers, while categorical fields are assessed for cardinality, top values, and null counts.

What is data profiling for numeric and categorical fields?▼

Data profiling evaluates numeric fields for statistical anomalies like standard deviation and outliers, and categorical fields for cardinality and null counts to understand data distributions.

Can I target my data quality assessment to specific fields in a table?▼

Yes, you can focus your data quality assessment on specific fields or restrict the profiling to only numeric or categorical data types for targeted analysis instead of a full table summary.

When do I need to run data profiling before anomaly detection?▼

You need data profiling before anomaly detection to establish typical value ranges, such as identifying unusually high or low amounts in GL transactions that might skew detection results.

Does table analysis work with both numeric and categorical data types?▼

Yes, table analysis supports both numeric fields for statistical insights like mean and standard deviation, and categorical fields for identifying top values and counting nulls.

Related Skills

Explore More Data & Analytics Skills→
DatarailsDatarails
3

dr-tables

List and explore Datarails Finance OS tables with schemas and field values.

Official
Basic
snoodleboot-iosnoodleboot-io
2

anomaly-detection-techniques

Detect anomalies and outliers in data sets using statistical analysis.

Official
Intermediate
lyticslytics
4

audience-snapshot

Analyze segment demographics and field distributions via the /api/segment/fieldinfo endpoint.

Official
Intermediate
DatarailsDatarails
3

dr-anomalies

Detect outliers, missing values, duplicates, and temporal issues in Datarails Finance OS tables.

Official
Intermediate