ag-logic-auditor

To audit Excel financial model formulas for contextual logic errors, you can use an automated auditing skill that checks formulas against row labels and business rules, identifying mathematically valid but contextually misaligned calculations. This requires a pre-extracted workbook JSON file.

Pattern break detection in financial models identifies formula inconsistencies across time series rows, flagging calculations that deviate from expected patterns. It also detects embedded hard-coded numeric literals that should be input cell references, preventing silent calculation errors.

You can check for hard-coded values in Excel financial models by running an automated formula audit that identifies embedded numeric literals within formulas. The audit flags these literals, which should typically be replaced with input cell references for proper financial modeling standards.

Yes, you can validate business rules and run sanity checks in project finance models to detect impossible values like negative tax on positive profit or DSCR anomalies. The auditing process cross-references formulas against documented rules and cross-sheet consistency.

Yes, you need to convert your Excel workbook into a pre-extracted JSON file using a shared extraction script before auditing. The formula auditing process requires this structured JSON format to parse formulas and output standardized findings with R1C1 notation.

The best way to find mathematically correct but contextually wrong Excel formulas is using an automated logic auditor that cross-references calculations with row descriptions and column headers. This detects misaligned business logic that manual reviews often miss.