Can this skill handle formulas that aren't just DR.GET?

Yes, it can trace derived Excel formulas (like SUM, addition, or subtraction) back to their DR.GET precedents to find the underlying source data.

What are the technical requirements for this skill?

You must have Python and the 'openpyxl' library installed, as well as an active connection to the Datarails Finance OS via the MCP connector.

Why does the skill perform total validation?

Total validation compares the sum of the retrieved records against the cached value in Excel to ensure the drill-down is accurate and no filters were missed.

How does the skill handle hidden filters in Excel?

It automatically scans the workbook for hidden worksheets like 'dr control' to extract JSON filter definitions, ensuring the drill-down matches the report's logic.

Can I choose which fields to break down by?

Yes, you can use the '--by' argument to specify comma-separated dimensions like 'Account Full' or 'Vendor' for your analysis.

Datarails Excel Drill-Down

Name: Datarails Excel Drill-Down
Author: Datarails

byDatarails

•

Analíticas y Monitorización

Drills down into Datarails Excel cells to extract and analyze underlying line-item detail and financial breakdowns.

The Datarails Excel Drill-Down skill enables deep financial analysis by resolving Datarails DR.GET formulas and complex Excel formula chains directly within Claude Code. It automates the process of identifying data sources, interpreting hidden global filters from 'dr control' worksheets, and querying the Datarails Finance OS for granular breakdowns. By cross-referencing workbook values with live database queries and validating totals for accuracy, this skill provides a transparent, audit-ready view of the transactional data powering high-level financial aggregates.

Características Principales

01Resolves direct DR.GET and derived Excel formulas to identify data precedents

02Automatically extracts global filter configurations from hidden 'dr control' sheets

03Performs automated total validation to ensure data integrity and accuracy

04Generates detailed breakdowns by dimensions like Account, Vendor, and Department

05Supports multi-source aggregation for derived calculations (e.g., Gross Profit)

062 GitHub stars

Casos de Uso

01Automating the extraction of department-level data from complex Datarails-connected workbooks

02Explaining variances in Excel models by breaking down aggregate cells into line-item details

03Auditing financial reports to find the specific transactions behind a consolidated balance

Características Principales

01Resolves direct DR.GET and derived Excel formulas to identify data precedents

02Automatically extracts global filter configurations from hidden 'dr control' sheets

03Performs automated total validation to ensure data integrity and accuracy

04Generates detailed breakdowns by dimensions like Account, Vendor, and Department

05Supports multi-source aggregation for derived calculations (e.g., Gross Profit)

062 GitHub stars

Casos de Uso

01Automating the extraction of department-level data from complex Datarails-connected workbooks

02Explaining variances in Excel models by breaking down aggregate cells into line-item details

03Auditing financial reports to find the specific transactions behind a consolidated balance