README

Creates, edits, and analyzes spreadsheet files (.xlsx, .xlsm, .csv, .tsv). Supports formula authoring, financial models with color coding, data cleaning, and format conversion. Recalculates formula values via LibreOffice.

Prerequisites

Install core packages:

pip install pandas openpyxl xlsxwriter

Scripts

Formula Recalculation

Excel formulas written by Python libraries store expressions but not computed values. LibreOffice recalculates and saves the values so recipients see correct results on open:

python scripts/recalc.py output.xlsx

The script auto-configures LibreOffice on first run, including in sandboxed environments.

Color Coding Convention

See references/standards.md for the full financial model color coding standard (hardcoded inputs, formulas, links, and assumptions).

SKILL.md

When to Activate

• User wants to create, edit, read, or fix a .xlsx, .xlsm, .csv, or .tsv file
• User needs to clean or restructure messy tabular data
• User wants to build a financial model with formulas and color coding
• User needs to convert between tabular file formats

Skip When

• User wants a Word document — use codi-docx
• User wants a PDF — use codi-pdf
• User wants a PowerPoint deck — use codi-pptx
• User wants a branded HTML report for PDF export — use codi-content-factory
• User wants to call the Google Sheets API — use a gspread or Google Sheets API flow

Requirements for Outputs

All Excel files

Professional Font

• Use a consistent, professional font (e.g., Arial, Times New Roman) for all deliverables unless otherwise instructed by the user

Zero Formula Errors

• Every Excel model MUST be delivered with ZERO formula errors (#REF!, #DIV/0!, #VALUE!, #N/A, #NAME?)

Preserve Existing Templates (when updating templates)

• Study and EXACTLY match existing format, style, and conventions when modifying files
• Never impose standardized formatting on files with established patterns
• Existing template conventions ALWAYS override these guidelines

Financial models

Read ${CLAUDE_SKILL_DIR}[[/references/standards.md]] for color coding conventions, number formatting standards, formula construction rules, and hardcode documentation requirements.

XLSX creation, editing, and analysis

Overview

A user may ask you to create, edit, or analyze the contents of an .xlsx file. You have different tools and workflows available for different tasks.

Important Requirements

LibreOffice Required for Formula Recalculation: You can assume LibreOffice is installed for recalculating formula values using the ${CLAUDE_SKILL_DIR}[[/scripts/recalc.py]] script. The script automatically configures LibreOffice on first run, including in sandboxed environments where Unix sockets are restricted (handled by ${CLAUDE_SKILL_DIR}[[/scripts/office/soffice.py]])

Reading and analyzing data

Data analysis with pandas

For data analysis, visualization, and basic operations, use pandas which provides powerful data manipulation capabilities:

import pandas as pd

# Read Excel
df = pd.read_excel('file.xlsx')  # Default: first sheet
all_sheets = pd.read_excel('file.xlsx', sheet_name=None)  # All sheets as dict

# Analyze
df.head()      # Preview data
df.info()      # Column info
df.describe()  # Statistics

# Write Excel
df.to_excel('output.xlsx', index=False)

Excel File Workflows

CRITICAL: Use Formulas, Not Hardcoded Values

Always use Excel formulas instead of calculating values in Python and hardcoding them. This ensures the spreadsheet remains dynamic and updateable.

WRONG - Hardcoding Calculated Values

# Bad: Calculating in Python and hardcoding result
total = df['Sales'].sum()
sheet['B10'] = total  # Hardcodes 5000

# Bad: Computing growth rate in Python
growth = (df.iloc[-1]['Revenue'] - df.iloc[0]['Revenue']) / df.iloc[0]['Revenue']
sheet['C5'] = growth  # Hardcodes 0.15

# Bad: Python calculation for average
avg = sum(values) / len(values)
sheet['D20'] = avg  # Hardcodes 42.5

CORRECT - Using Excel Formulas

# Good: Let Excel calculate the sum
sheet['B10'] = '=SUM(B2:B9)'

# Good: Growth rate as Excel formula
sheet['C5'] = '=(C4-C2)/C2'

# Good: Average using Excel function
sheet['D20'] = '=AVERAGE(D2:D19)'

This applies to ALL calculations - totals, percentages, ratios, differences, etc. The spreadsheet should be able to recalculate when source data changes.

Common Workflow

1. Choose tool: pandas for data, openpyxl for formulas/formatting
2. Create/Load: Create new workbook or load existing file
3. Modify: Add/edit data, formulas, and formatting
4. Save: Write to file
5. Recalculate formulas (MANDATORY IF USING FORMULAS): Use the ${CLAUDE_SKILL_DIR}[[/scripts/recalc.py]] script

   python ${CLAUDE_SKILL_DIR}[[/scripts/recalc.py]] output.xlsx

6. Verify and fix any errors:
   • The script returns JSON with error details
   • If status is errors_found, check error_summary for specific error types and locations
   • Fix the identified errors and recalculate again
   • Common errors to fix:
     • #REF!: Invalid cell references
     • #DIV/0!: Division by zero
     • #VALUE!: Wrong data type in formula
     • #NAME?: Unrecognized formula name

Creating Branded Output

When the user asks to create a branded XLSX, ask two questions if not already stated:

Step 1 — Brand (skip if brand already named):

Which brand styling would you like to apply?
  1. CODI (default — uses bundled tokens)
  2. Custom — provide a path to brand_tokens.json

Step 2 — Theme (skip if theme already named):

Which color theme?
  1. Dark (default)
  2. Light

Then run (detect runtime first):

if command -v npx &>/dev/null && npx tsx --version &>/dev/null 2>&1; then
  # TypeScript (preferred)
  npx tsx ${CLAUDE_SKILL_DIR}[[/scripts/ts/generate_xlsx.ts]] --content content.json --tokens /path/to/brand_tokens.json --theme dark --output output.xlsx
elif command -v uv &>/dev/null; then
  # Python via uv (ephemeral isolated env — no system pollution)
  uv run --with openpyxl python3 ${CLAUDE_SKILL_DIR}[[/scripts/python/generate_xlsx.py]] --content content.json --tokens /path/to/brand_tokens.json --theme dark --output output.xlsx
else
  # Python via venv fallback
  SKILL_VENV="/tmp/codi-skill-venv" && python3 -m venv "$SKILL_VENV" 2>/dev/null || true
  "$SKILL_VENV/bin/pip" install -q openpyxl
  "$SKILL_VENV/bin/python3" ${CLAUDE_SKILL_DIR}[[/scripts/python/generate_xlsx.py]] --content content.json --tokens /path/to/brand_tokens.json --theme dark --output output.xlsx
fi

Omit --tokens to use Codi default brand. Replace dark with light for the light theme.

Creating new Excel files

# Using openpyxl for formulas and formatting
from openpyxl import Workbook
from openpyxl.styles import Font, PatternFill, Alignment

wb = Workbook()
sheet = wb.active

# Add data
sheet['A1'] = 'Hello'
sheet['B1'] = 'World'
sheet.append(['Row', 'of', 'data'])

# Add formula
sheet['B2'] = '=SUM(A1:A10)'

# Formatting
sheet['A1'].font = Font(bold=True, color='FF0000')
sheet['A1'].fill = PatternFill('solid', start_color='FFFF00')
sheet['A1'].alignment = Alignment(horizontal='center')

# Column width
sheet.column_dimensions['A'].width = 20

wb.save('output.xlsx')

Editing existing Excel files

# Using openpyxl to preserve formulas and formatting
from openpyxl import load_workbook

# Load existing file
wb = load_workbook('existing.xlsx')
sheet = wb.active  # or wb['SheetName'] for specific sheet

# Working with multiple sheets
for sheet_name in wb.sheetnames:
    sheet = wb[sheet_name]
    print(f"Sheet: {sheet_name}")

# Modify cells
sheet['A1'] = 'New Value'
sheet.insert_rows(2)  # Insert row at position 2
sheet.delete_cols(3)  # Delete column 3

# Add new sheet
new_sheet = wb.create_sheet('NewSheet')
new_sheet['A1'] = 'Data'

wb.save('modified.xlsx')

Recalculating formulas

Excel files created or modified by openpyxl contain formulas as strings but not calculated values. Use the provided ${CLAUDE_SKILL_DIR}[[/scripts/recalc.py]] script to recalculate formulas:

python ${CLAUDE_SKILL_DIR}[[/scripts/recalc.py]] <excel_file> [timeout_seconds]

Example:

python ${CLAUDE_SKILL_DIR}[[/scripts/recalc.py]] output.xlsx 30

The script:

• Automatically sets up LibreOffice macro on first run
• Recalculates all formulas in all sheets
• Scans ALL cells for Excel errors (#REF!, #DIV/0!, etc.)
• Returns JSON with detailed error locations and counts
• Works on both Linux and macOS

Formula Verification Checklist

Quick checks to ensure formulas work correctly:

Essential Verification

• Test 2-3 sample references: Verify they pull correct values before building full model
• Column mapping: Confirm Excel columns match (e.g., column 64 = BL, not BK)
• Row offset: Remember Excel rows are 1-indexed (DataFrame row 5 = Excel row 6)

Common Pitfalls

• NaN handling: Check for null values with pd.notna()
• Far-right columns: FY data often in columns 50+
• Multiple matches: Search all occurrences, not just first
• Division by zero: Check denominators before using / in formulas (#DIV/0!)
• Wrong references: Verify all cell references point to intended cells (#REF!)
• Cross-sheet references: Use correct format (Sheet1!A1) for linking sheets

Formula Testing Strategy

• Start small: Test formulas on 2-3 cells before applying broadly
• Verify dependencies: Check all cells referenced in formulas exist
• Test edge cases: Include zero, negative, and very large values

Interpreting ${CLAUDE_SKILL_DIR}[[/scripts/recalc.py]] Output

The script returns JSON with error details:

{
  "status": "success",           // or "errors_found"
  "total_errors": 0,              // Total error count
  "total_formulas": 42,           // Number of formulas in file
  "error_summary": {              // Only present if errors found
    "#REF!": {
      "count": 2,
      "locations": ["Sheet1!B5", "Sheet1!C10"]
    }
  }
}

Best Practices

Library Selection

• pandas: Best for data analysis, bulk operations, and simple data export
• openpyxl: Best for complex formatting, formulas, and Excel-specific features

Working with openpyxl

• Cell indices are 1-based (row=1, column=1 refers to cell A1)
• Use data_only=True to read calculated values: load_workbook('file.xlsx', data_only=True)
• Warning: If opened with data_only=True and saved, formulas are replaced with values and permanently lost
• For large files: Use read_only=True for reading or write_only=True for writing
• Formulas are preserved but not evaluated - use ${CLAUDE_SKILL_DIR}[[/scripts/recalc.py]] to update values

Working with pandas

• Specify data types to avoid inference issues: pd.read_excel('file.xlsx', dtype={'id': str})
• For large files, read specific columns: pd.read_excel('file.xlsx', usecols=['A', 'C', 'E'])
• Handle dates properly: pd.read_excel('file.xlsx', parse_dates=['date_column'])

Code Style Guidelines

IMPORTANT: When generating Python code for Excel operations:

• Write minimal, concise Python code without unnecessary comments
• Avoid verbose variable names and redundant operations
• Avoid unnecessary print statements

For Excel files themselves:

• Add comments to cells with complex formulas or important assumptions
• Document data sources for hardcoded values
• Include notes for key calculations and model sections