Agent skill

Excel Analysis

Analyze Excel spreadsheets, create pivot tables, generate charts, and perform data analysis. Use when analyzing Excel files, spreadsheets, tabular data, or .xlsx files.

View SKILL.md on GitHub Repository
Stars 18
Forks 10

Install this agent skill to your Project

npx add-skill https://github.com/kivilaid/plugin-marketplace/tree/main/plugins/davila7-claude-code-templates/cli-tool/components/skills/enterprise-communication/excel-analysis

SKILL.md

Excel Analysis

Quick start

Read Excel files with pandas:

python
import pandas as pd

# Read Excel file
df = pd.read_excel("data.xlsx", sheet_name="Sheet1")

# Display first few rows
print(df.head())

# Basic statistics
print(df.describe())

Reading multiple sheets

Process all sheets in a workbook:

python
import pandas as pd

# Read all sheets
excel_file = pd.ExcelFile("workbook.xlsx")

for sheet_name in excel_file.sheet_names:
    df = pd.read_excel(excel_file, sheet_name=sheet_name)
    print(f"\n{sheet_name}:")
    print(df.head())

Data analysis

Perform common analysis tasks:

python
import pandas as pd

df = pd.read_excel("sales.xlsx")

# Group by and aggregate
sales_by_region = df.groupby("region")["sales"].sum()
print(sales_by_region)

# Filter data
high_sales = df[df["sales"] > 10000]

# Calculate metrics
df["profit_margin"] = (df["revenue"] - df["cost"]) / df["revenue"]

# Sort by column
df_sorted = df.sort_values("sales", ascending=False)

Creating Excel files

Write data to Excel with formatting:

python
import pandas as pd

df = pd.DataFrame({
    "Product": ["A", "B", "C"],
    "Sales": [100, 200, 150],
    "Profit": [20, 40, 30]
})

# Write to Excel
writer = pd.ExcelWriter("output.xlsx", engine="openpyxl")
df.to_excel(writer, sheet_name="Sales", index=False)

# Get worksheet for formatting
worksheet = writer.sheets["Sales"]

# Auto-adjust column widths
for column in worksheet.columns:
    max_length = 0
    column_letter = column[0].column_letter
    for cell in column:
        if len(str(cell.value)) > max_length:
            max_length = len(str(cell.value))
    worksheet.column_dimensions[column_letter].width = max_length + 2

writer.close()

Pivot tables

Create pivot tables programmatically:

python
import pandas as pd

df = pd.read_excel("sales_data.xlsx")

# Create pivot table
pivot = pd.pivot_table(
    df,
    values="sales",
    index="region",
    columns="product",
    aggfunc="sum",
    fill_value=0
)

print(pivot)

# Save pivot table
pivot.to_excel("pivot_report.xlsx")

Charts and visualization

Generate charts from Excel data:

python
import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_excel("data.xlsx")

# Create bar chart
df.plot(x="category", y="value", kind="bar")
plt.title("Sales by Category")
plt.xlabel("Category")
plt.ylabel("Sales")
plt.tight_layout()
plt.savefig("chart.png")

# Create pie chart
df.set_index("category")["value"].plot(kind="pie", autopct="%1.1f%%")
plt.title("Market Share")
plt.ylabel("")
plt.savefig("pie_chart.png")

Data cleaning

Clean and prepare Excel data:

python
import pandas as pd

df = pd.read_excel("messy_data.xlsx")

# Remove duplicates
df = df.drop_duplicates()

# Handle missing values
df = df.fillna(0)  # or df.dropna()

# Remove whitespace
df["name"] = df["name"].str.strip()

# Convert data types
df["date"] = pd.to_datetime(df["date"])
df["amount"] = pd.to_numeric(df["amount"], errors="coerce")

# Save cleaned data
df.to_excel("cleaned_data.xlsx", index=False)

Merging and joining

Combine multiple Excel files:

python
import pandas as pd

# Read multiple files
df1 = pd.read_excel("sales_q1.xlsx")
df2 = pd.read_excel("sales_q2.xlsx")

# Concatenate vertically
combined = pd.concat([df1, df2], ignore_index=True)

# Merge on common column
customers = pd.read_excel("customers.xlsx")
sales = pd.read_excel("sales.xlsx")

merged = pd.merge(sales, customers, on="customer_id", how="left")

merged.to_excel("merged_data.xlsx", index=False)

Advanced formatting

Apply conditional formatting and styles:

python
import pandas as pd
from openpyxl import load_workbook
from openpyxl.styles import PatternFill, Font

# Create Excel file
df = pd.DataFrame({
    "Product": ["A", "B", "C"],
    "Sales": [100, 200, 150]
})

df.to_excel("formatted.xlsx", index=False)

# Load workbook for formatting
wb = load_workbook("formatted.xlsx")
ws = wb.active

# Apply conditional formatting
red_fill = PatternFill(start_color="FF0000", end_color="FF0000", fill_type="solid")
green_fill = PatternFill(start_color="00FF00", end_color="00FF00", fill_type="solid")

for row in range(2, len(df) + 2):
    cell = ws[f"B{row}"]
    if cell.value < 150:
        cell.fill = red_fill
    else:
        cell.fill = green_fill

# Bold headers
for cell in ws[1]:
    cell.font = Font(bold=True)

wb.save("formatted.xlsx")

Performance tips

  • Use read_excel with usecols to read specific columns only
  • Use chunksize for very large files
  • Consider using engine='openpyxl' or engine='xlrd' based on file type
  • Use dtype parameter to specify column types for faster reading

Available packages

  • pandas - Data analysis and manipulation (primary)
  • openpyxl - Excel file creation and formatting
  • xlrd - Reading older .xls files
  • xlsxwriter - Advanced Excel writing capabilities
  • matplotlib - Chart generation

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

kivilaid/plugin-marketplace

file-organizer

Intelligently organizes your files and folders across your computer by understanding context, finding duplicates, suggesting better structures, and automating cleanup tasks. Reduces cognitive load and keeps your digital workspace tidy without manual effort.

18 10
Explore
kivilaid/plugin-marketplace

brainstorming

Use when creating or developing anything, before writing code or implementation plans - refines rough ideas into fully-formed designs through structured Socratic questioning, alternative exploration, and incremental validation

18 10
Explore
kivilaid/plugin-marketplace

sast-configuration

Configure Static Application Security Testing (SAST) tools for automated vulnerability detection in application code. Use when setting up security scanning, implementing DevSecOps practices, or automating code vulnerability detection.

18 10
Explore
kivilaid/plugin-marketplace

test-driven-development

Use when implementing any feature or bugfix, before writing implementation code - write the test first, watch it fail, write minimal code to pass; ensures tests actually verify behavior by requiring failure first

18 10
Explore
kivilaid/plugin-marketplace

meeting-insights-analyzer

Analyzes meeting transcripts and recordings to uncover behavioral patterns, communication insights, and actionable feedback. Identifies when you avoid conflict, use filler words, dominate conversations, or miss opportunities to listen. Perfect for professionals seeking to improve their communication and leadership skills.

18 10
Explore
kivilaid/plugin-marketplace

internal-comms

A set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever asked to write some sort of internal communications (status reports, leadership updates, 3P updates, company newsletters, FAQs, incident reports, project updates, etc.).

18 10
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results