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TheArchitectitTheArchitectit/awesome-cline-skills/skills/xlsx/SKILL.md

xlsx

Skill8 starsChanged 3 months agoApache-2.0View on GitHub

Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. This skill should be used when Cline needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv) for creating new spreadsheets with formulas and formatting, reading or analyzing data, modifying existing spreadsheets while preserving formulas, or performing data analysis and visualization in spreadsheets.

  • Installs packages

We split this skill into the rules it gives an agent

20 rules. 4 are common, 11 are distinctive, and 5 are very specific to this project.

  • 4

    Common

    Most of the 188 edited versions we read keep these.

  • 11

    Distinctive

    This author's own choices. Worth a look.

  • 5

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Use pandas for quick data analysis or openpyxl with data_only=True for calculated values.

    line 24
  2. Distinctive:

    Use openpyxl for formula and formatting support.

    line 27
  3. Distinctive:

    Use openpyxl to load, modify, and save while preserving structure.

    line 30
  4. Distinctive:

    Use pandas for analysis, then openpyxl for formatted output.

    line 33
  5. Very specific:

    If you open a file with data_only=True and save it, formulas are permanently replaced with their calculated values.

    line 119
  6. Distinctive:

    Always use Excel formulas instead of calculating values in Python and hardcoding them.

    line 123
  7. Common:

    Place ALL assumptions in separate cells (growth rates, margins, multiples)

    line 159
  8. Very specific:

    Use cell references, not hardcoded values: =B5*(1+$B$6) not =B5*1.05

    line 160
  9. Distinctive:

    Document hardcoded values with comments: "Source: Company 10-K, FY2024, Page 45"

    line 161
  10. Distinctive:

    Before delivering any spreadsheet:

    line 202
  11. Distinctive:

    Library selection: Default to pandas for data tasks, openpyxl for formatting/formulas

    line 216
  12. Distinctive:

    Always use formulas: Never hardcode calculated values — the spreadsheet must remain dynamic

    line 217
  13. Distinctive:

    Verify before delivery: Run formula checks and validate data types

    line 218
  14. Distinctive:

    Preserve existing work: When editing, study existing conventions before modifying

    line 219
  15. Distinctive:

    Recalculate after changes: If LibreOffice is available, use it to recalculate formula values after openpyxl edits

    line 220
  16. Very specific:

    Large files: Use read_only=True for reading large files, write_only=True for creating them

    line 221
  17. Common:

    Write concise, minimal Python code

    line 225
  18. Common:

    Avoid verbose variable names and redundant operations

    line 226
  19. Common:

    Avoid unnecessary print statements

    line 227
  20. Very specific:

    Use context managers (with statements) for file operations

    line 228

“Common” is measured: kept by at least 6 in 10 of the 188 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

XLSX Creation, Editing, and Analysis

Overview

This skill enables Cline to create, edit, and analyze Excel spreadsheets using Python libraries. Different workflows are available depending on the task: pandas for data analysis and bulk operations, openpyxl for formulas, formatting, and Excel-specific features.

When to Use This Skill

  • Creating new spreadsheets with formulas, charts, and formatting
  • Reading or analyzing data from existing Excel files
  • Modifying spreadsheets while preserving formulas and formatting
  • Building financial models with proper conventions
  • Data transformation and visualization in spreadsheet format
  • Converting data between formats (CSV, TSV → XLSX)

Workflow Decision Tree

Reading/Analyzing Data

Use pandas for quick data analysis or openpyxl with data_only=True for calculated values.

Creating New Spreadsheet

Use openpyxl for formula and formatting support.

Editing Existing Spreadsheet

Use openpyxl to load, modify, and save while preserving structure.

Data Analysis & Visualization

Use pandas for analysis, then openpyxl for formatted output.

Reading and Analyzing Data

Quick Data Analysis with pandas

import pandas as pd

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

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

Reading with openpyxl

from openpyxl import load_workbook

wb = load_workbook('file.xlsx', data_only=True)  # Read calculated values
wb = load_workbook('file.xlsx')  # Read formulas (default)

for sheet_name in wb.sheetnames:
    sheet = wb[sheet_name]
    for row in sheet.iter_rows(values_only=True):
        print(row)

Creating New Spreadsheets

from openpyxl import Workbook
from openpyxl.styles import Font, PatternFill, Alignment, Border, Side

wb = Workbook()
sheet = wb.active
sheet.title = "Summary"

# Add headers
headers = ['Item', 'Q1', 'Q2', 'Q3', 'Q4', 'Total']
for col, header in enumerate(headers, 1):
    cell = sheet.cell(row=1, column=col, value=header)
    cell.font = Font(bold=True)

# Add data and formulas
data = [['Revenue', 100, 120, 130, 150], ['Expenses', 80, 85, 90, 95]]
for row_idx, row_data in enumerate(data, 2):
    for col_idx, value in enumerate(row_data, 1):
        sheet.cell(row=row_idx, column=col_idx, value=value)
    # Formula for total using SUM
    sheet.cell(row=row_idx, column=6, value=f'=SUM(B{row_idx}:E{row_idx})')

# Formatting
sheet.column_dimensions['A'].width = 20
for col in 'BCDEF':
    sheet.column_dimensions[col].width = 12

wb.save('report.xlsx')

Editing Existing Spreadsheets

from openpyxl import load_workbook

wb = load_workbook('existing.xlsx')
sheet = wb.active

# Modify cells
sheet['A1'] = 'Updated Header'

# Insert rows/columns
sheet.insert_rows(2)
sheet.delete_cols(3)

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

wb.save('modified.xlsx')

⚠️ Warning: data_only=True

If you open a file with data_only=True and save it, formulas are permanently replaced with their calculated values. Only use data_only=True for reading.

CRITICAL: Use Formulas, Not Hardcoded Values

Always use Excel formulas instead of calculating values in Python and hardcoding them.

❌ WRONG — Hardcoding

total = df['Sales'].sum()
sheet['B10'] = total  # Hardcodes 5000

✅ CORRECT — Excel Formula

sheet['B10'] = '=SUM(B2:B9)'  # Dynamic, recalculates

Financial Model Standards

Color Coding Conventions

ColorRGBUsage
Blue text(0,0,255)Hardcoded inputs, assumptions
Black text(0,0,0)All formulas and calculations
Green text(0,128,0)Cross-sheet links within workbook
Red text(255,0,0)External links to other files
Yellow background(255,255,0)Key assumptions needing attention

Number Formatting

  • Years: Format as text strings ("2024" not 2,024)
  • Currency: $#,##0 with units in headers ("Revenue ($mm)")
  • Zeros: Display as "-" using number formatting
  • Percentages: Default 0.0% format
  • Multiples: 0.0x format (EV/EBITDA, P/E)
  • Negatives: Parentheses (123) not minus -123

Formula Construction

  • Place ALL assumptions in separate cells (growth rates, margins, multiples)
  • Use cell references, not hardcoded values: =B5*(1+$B$6) not =B5*1.05
  • Document hardcoded values with comments: "Source: Company 10-K, FY2024, Page 45"

Adding Charts

from openpyxl.chart import BarChart, Reference

chart = BarChart()
chart.title = "Quarterly Revenue"
chart.type = "col"

data = Reference(sheet, min_col=2, min_row=1, max_col=5, max_row=3)
cats = Reference(sheet, min_col=1, min_row=2, max_row=3)
chart.add_data(data, titles_from_data=True)
chart.set_categories(cats)

sheet.add_chart(chart, "A8")

Data Transforms with pandas

import pandas as pd

# Pivot table
df = pd.read_excel('raw_data.xlsx')
pivot = df.pivot_table(values='Revenue', index='Region', columns='Quarter', aggfunc='sum')
pivot.to_excel('pivot_output.xlsx')

# Merge multiple sheets
sheets = pd.read_excel('multi.xlsx', sheet_name=None)
combined = pd.concat(sheets.values(), ignore_index=True)
combined.to_excel('combined.xlsx', index=False)

# Filter and export
filtered = df[df['Status'] == 'Active'][['Name', 'Email', 'Revenue']]
filtered.to_excel('active_clients.xlsx', index=False)

Formula Verification Checklist

Before delivering any spreadsheet:

  • Test 2-3 sample formula references for correct values
  • Verify column mapping (column 64 = BL, not BK)
  • Remember row offset: DataFrame row 5 = Excel row 6 (1-indexed)
  • Check for NaN with pd.notna() before writing
  • Prevent division by zero in formulas
  • Verify all cell references point to intended cells
  • Use Sheet1!A1 format for cross-sheet references
  • Test with edge cases (zero, negative, very large values)

Cline Workflow Notes

  1. Install location: Copy this skill directory to .cline/skills/xlsx/ (project-level) or ~/.cline/skills/xlsx/ (global)
  2. Library selection: Default to pandas for data tasks, openpyxl for formatting/formulas
  3. Always use formulas: Never hardcode calculated values — the spreadsheet must remain dynamic
  4. Verify before delivery: Run formula checks and validate data types
  5. Preserve existing work: When editing, study existing conventions before modifying
  6. Recalculate after changes: If LibreOffice is available, use it to recalculate formula values after openpyxl edits
  7. Large files: Use read_only=True for reading large files, write_only=True for creating them

Code Style

  • Write concise, minimal Python code
  • Avoid verbose variable names and redundant operations
  • Avoid unnecessary print statements
  • Use context managers (with statements) for file operations

Dependencies

pip install openpyxl pandas
# Optional for advanced features:
pip install xlsxwriter xlrd

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