xlsx
Comprehensive spreadsheet creation, editing, and analysis with support
When & Why to Use This Skill
This Claude skill provides a comprehensive suite for professional Excel and spreadsheet management, enabling seamless creation, editing, and deep data analysis. By leveraging industry-standard libraries like pandas and openpyxl, it ensures high-performance data handling, sophisticated cell styling, and the implementation of dynamic formulas over static values. It is specifically optimized for financial modeling accuracy and scientific data integration, making it an essential tool for automated reporting and complex data workflows.
Use Cases
- Automated Financial Modeling: Generate professional-grade financial statements with standardized formatting (e.g., color-coded inputs and formulas) and dynamic Excel calculations for real-time updates.
- Large-Scale Data Analysis: Read and process multi-sheet workbooks using pandas to extract statistical insights, perform data cleaning, and generate summary reports automatically.
- Programmatic Spreadsheet Editing: Batch update existing Excel files by inserting rows, modifying cell values, and applying consistent branding or formatting across hundreds of documents.
- Scientific Research Integration: Connect spreadsheet data with advanced research ecosystems, including graph theory analysis and bibliographic reference management for academic workflows.
| name | xlsx |
|---|---|
| description | Comprehensive spreadsheet creation, editing, and analysis with support |
| version | 1.0.0 |
Excel/Spreadsheet Processing
Reading and Analyzing Data
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)
Creating Excel Files with openpyxl
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 - ALWAYS use formulas, not hardcoded values
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 Files
from openpyxl import load_workbook
wb = load_workbook('existing.xlsx')
sheet = wb.active
# Modify cells
sheet['A1'] = 'New Value'
sheet.insert_rows(2)
sheet.delete_cols(3)
# Add new sheet
new_sheet = wb.create_sheet('NewSheet')
new_sheet['A1'] = 'Data'
wb.save('modified.xlsx')
Critical: Use Formulas, Not Hardcoded Values
# BAD - Hardcoding calculated values
total = df['Sales'].sum()
sheet['B10'] = total # Hardcodes 5000
# GOOD - Using Excel formulas
sheet['B10'] = '=SUM(B2:B9)'
sheet['C5'] = '=(C4-C2)/C2' # Growth rate
sheet['D20'] = '=AVERAGE(D2:D19)'
Financial Model Standards
- Blue text: Hardcoded inputs
- Black text: ALL formulas
- Green text: Links from other worksheets
- Yellow background: Key assumptions
Best Practices
- Use
data_only=Trueto read calculated values - For large files: Use
read_only=Trueorwrite_only=True - Formulas are preserved but not evaluated by openpyxl
Scientific Skill Interleaving
This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:
Graph Theory
- networkx [○] via bicomodule
- Universal graph hub
Bibliography References
general: 734 citations in bib.duckdb
Cat# Integration
This skill maps to Cat# = Comod(P) as a bicomodule in the equipment structure:
Trit: 0 (ERGODIC)
Home: Prof
Poly Op: ⊗
Kan Role: Adj
Color: #26D826
GF(3) Naturality
The skill participates in triads satisfying:
(-1) + (0) + (+1) ≡ 0 (mod 3)
This ensures compositional coherence in the Cat# equipment structure.