Excel Formula Analyzer

Excel Formula Analyzer

Quick Start

To trace formula dependencies in an Excel workbook:

python .claude/​skills/​excel-formula-analyzer/​scripts/​trace-formula-dag.py "Workbook.xlsx" "Sheet Name" \
    --table MyTable \
    --columns 16-36,57,58,59

Sample output:

[Col 36] End of Harvest
     = =IF([@[Actual End of Harvest]]="",[@[Expected End of Harvest]],[@[Actual ...
  Depends on: ['Actual End of Harvest', 'Expected End of Harvest']
  |- [31] Expected End of Harvest
       = =[@[Expected Beginning of Harvest]]+[@[Harvest Window]]+[@[Additional ...
       |- [28] Expected Beginning of Harvest (calculated)
       |- [59] Harvest Window (INPUT)
       |- [33] Additional Days of Harvest (INPUT)

Overview

This skill enables analysis of Excel spreadsheet formulas to understand how calculations flow from inputs to outputs. It builds dependency graphs (DAGs) from formula references and can help create equivalent code implementations.

When to Use This Skill

• Tracing how a specific cell's value is calculated
• Building a complete dependency graph of formula relationships
• Reverse-engineering business logic embedded in spreadsheets
• Creating TypeScript/​Python implementations that replicate Excel calculations
• Validating that code implementations match Excel behavior

Core Workflow

Step 1: Load the Workbook

Load with data_only=False to get formulas instead of computed values:

import openpyxl
wb = openpyxl.load_workbook("spreadsheet.xlsx", data_only=False)
ws = wb["SheetName"]

Step 2: Parse Structured Table References

Excel tables use structured references like TableName[[#This Row],[Column]]. See references/​structured-refs.md for complete syntax details.

Key patterns:

• Table[[#This Row],[Column]] - Same-row reference
• Table[Column] - Full column reference (used in XLOOKUP)

Step 3: Build the DAG

Run the trace script to analyze dependencies:

python .claude/​skills/​excel-formula-analyzer/​scripts/​trace-formula-dag.py "file.xlsx" "Sheet" --columns 16-36

Step 4: Implement in Code

Translate formulas to TypeScript/​Python using patterns in references/​formula-patterns.md.

What Didn't Work (Lessons Learned)

The formulas Library

The Python formulas library can parse Excel formulas into ASTs, but fails on structured table references:

Error: Not a valid formula: =MEDIAN(Crops[Revenue - Copy])

The library expects A1-style references and cannot handle TableName[Column] syntax.

xlwings COM Interface

xlwings can use Excel's native DirectPrecedents API, but:

• Requires Excel to be installed (Windows/​Mac only)
• Can timeout on large workbooks
• COM interface can be unreliable

Converting Table References to A1 Notation

While Excel can convert structured refs to A1 notation (Table menu > Convert to Range), this destroys the semantic meaning and makes the formulas harder to understand.

Key Nuances

Excel Date Handling

Excel stores dates as numbers (days since 1900-01-01, with a leap year bug):

from datetime import datetime, timedelta

def excel_to_date(excel_num):
    if excel_num is None or excel_num < 1:
        return None
    return datetime(1899, 12, 30) + timedelta(days=excel_num)

Formula vs Value Reading

• data_only=True: Returns calculated values (requires the file was saved with calculations)
• data_only=False: Returns formulas as strings

To get both, load the workbook twice.

COALESCE Patterns

Excel implements COALESCE with IF(ISBLANK()). In code, use nullish coalescing:

const value = actualValue ?? plannedValue;

Resources

scripts/

• trace-formula-dag.py - Build formula DAGs with CLI arguments for table name and columns
• validate-calculator.py - Validate code implementations against Excel values

references/

• structured-refs.md - Complete guide to Excel structured table reference syntax, parsing patterns, and gotchas
• formula-patterns.md - Common Excel formulas and their TypeScript/​Python equivalents