Why does the skill test for aggregation compatibility?

It tests fields against the Datarails aggregation API to identify which fields support fast, high-performance queries (~5 seconds) versus slower full-table scans.

Do I need to manually enter my table IDs?

No, the skill automatically scans your environment and identifies candidates for financial cubes and KPI tables based on row counts and naming conventions.

What is the main purpose of the /dr-learn skill?

It automates the discovery of Datarails table schemas and field mappings to create a profile that allows other skills, like /dr-extract, to work across any Datarails environment without hardcoding values.

What happens if the field mapping is incorrect?

The skill includes an interactive verification step where you can confirm, edit, or manually override the discovered mappings before the profile is saved.

Datarails Financial Table Discovery

Name: Datarails Financial Table Discovery
Author: Datarails

byDatarails

0•

Analytics & Monitoring

Automatically discovers and maps Datarails financial table structures to create reusable environment profiles.

This skill automates the configuration process for Datarails financial data extraction by scanning environment tables, identifying key financial cubes and KPI metrics, and mapping semantic fields like amounts, scenarios, and account hierarchies. It ensures compatibility with downstream extraction tools by testing aggregation capabilities and saving a structured client profile, eliminating the need for hardcoded values when switching between different Datarails instances or client environments.

Key Features

01Semantic mapping for amount, date, scenario, and account fields

02Automated identification of P&L cubes and KPI tables

03Interactive verification of discovered schemas and hierarchies

04Persistent JSON profile generation for cross-skill integration

050 GitHub stars

06Real-time aggregation compatibility testing and field validation

Use Cases

01Verifying field mappings and aggregation support for high-performance queries

02Standardizing data extraction logic across disparate table schemas

03Onboarding a new Datarails client environment for automated financial reporting

What are Skills?·How to Install

Install with 🐟 Skill.Fish

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

For use in Claude.ai and ChatGPT

Key Features

01Semantic mapping for amount, date, scenario, and account fields

02Automated identification of P&L cubes and KPI tables

03Interactive verification of discovered schemas and hierarchies

04Persistent JSON profile generation for cross-skill integration

050 GitHub stars

06Real-time aggregation compatibility testing and field validation

Use Cases

01Verifying field mappings and aggregation support for high-performance queries

02Standardizing data extraction logic across disparate table schemas

03Onboarding a new Datarails client environment for automated financial reporting

What are Skills?·How to Install

Install with 🐟 Skill.Fish

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

For use in Claude.ai and ChatGPT