excel-processor

Getting Started

1. Install the skill using the command above
2. Open your AI coding agent (Claude Code, Codex, Gemini CLI, or Cursor)
3. Reference the skill in your prompt
4. The AI will use the skill's capabilities automatically

Example Prompts

• "Analyze the sales data in revenue.csv and identify trends"
• "Create a visualization comparing Q1 vs Q2 performance metrics"

Overview

Read, transform, analyze, and generate Excel and CSV files using Python. This skill covers data loading, cleaning, filtering, aggregation, formula generation, and export to multiple formats.

Instructions

When a user asks you to work with spreadsheets, Excel files, or CSV data, follow these steps:

Step 1: Load the data

import pandas as pd

# For Excel files
df = pd.read_excel("data.xlsx", sheet_name=0)  # or sheet_name="Sheet1"

# For CSV files
df = pd.read_csv("data.csv")

# Show shape and preview
print(f"Shape: {df.shape[0]} rows x {df.shape[1]} columns")
print(f"Columns: {list(df.columns)}")
print(df.head())

Always print the shape, column names, and first few rows so the user can verify the data loaded correctly.

Step 2: Assess data quality

# Check for issues
print(f"Missing values:\n{df.isnull().sum()}")
print(f"\nDuplicates: {df.duplicated().sum()}")
print(f"\nData types:\n{df.dtypes}")

Report any issues found before proceeding with transformations.

Step 3: Apply requested transformations

Common operations:

Filtering:

filtered = df[df["status"] == "active"]
filtered = df[df["amount"] > 1000]
filtered = df[df["date"].between("2024-01-01", "2024-12-31")]

Aggregation:

summary = df.groupby("category").agg(
    count=("id", "count"),
    total=("amount", "sum"),
    average=("amount", "mean")
).reset_index()

Pivot tables:

pivot = df.pivot_table(
    values="revenue",
    index="region",
    columns="quarter",
    aggfunc="sum",
    margins=True
)

Cleaning:

df["name"] = df["name"].str.strip().str.title()
df["email"] = df["email"].str.lower()
df["date"] = pd.to_datetime(df["date"], errors="coerce")
df = df.drop_duplicates(subset=["id"])
df = df.dropna(subset=["required_field"])

Step 4: Export results

# To Excel with formatting
with pd.ExcelWriter("output.xlsx", engine="openpyxl") as writer:
    df.to_excel(writer, sheet_name="Data", index=False)
    summary.to_excel(writer, sheet_name="Summary", index=False)

# To CSV
df.to_csv("output.csv", index=False)

Step 5: Report what was done

Always summarize the operations performed, rows affected, and output file location.

Examples

Example 1: Clean and deduplicate a customer list

User request: "Clean up customers.xlsx -- remove duplicates, fix phone formatting, and split into active/inactive sheets"

Actions taken:

1. Load customers.xlsx (2,340 rows, 8 columns)
2. Remove 156 duplicate rows based on email
3. Standardize phone numbers to (XXX) XXX-XXXX format
4. Split into active (1,847 rows) and inactive (337 rows)
5. Export to customers_clean.xlsx with two sheets

Output:

Loaded: 2,340 rows x 8 columns
Removed: 156 duplicate rows (by email)
Fixed: 892 phone numbers reformatted
Split: 1,847 active, 337 inactive

Saved to customers_clean.xlsx:
  - Sheet "Active": 1,847 rows
  - Sheet "Inactive": 337 rows

Example 2: Generate a monthly sales summary

User request: "Create a pivot table from sales.csv showing revenue by region and month"

Actions taken:

1. Load sales.csv (15,200 rows)
2. Parse date column, extract month
3. Build pivot table: regions as rows, months as columns, sum of revenue
4. Add row and column totals
5. Export to sales_summary.xlsx

Output:

| Region    | Jan      | Feb      | Mar      | Total     |
|-----------|----------|----------|----------|-----------|
| North     | $45,200  | $52,100  | $48,900  | $146,200  |
| South     | $38,700  | $41,300  | $44,600  | $124,600  |
| East      | $51,900  | $49,800  | $55,200  | $156,900  |
| West      | $42,100  | $46,700  | $43,500  | $132,300  |
| Total     | $177,900 | $189,900 | $192,200 | $560,000  |

Saved to sales_summary.xlsx

Guidelines

• Always show data shape and column names after loading so the user can verify correctness.
• When dealing with dates, explicitly parse them with pd.to_datetime() and specify the format when ambiguous (e.g., is 01/02/03 Jan 2 or Feb 1?).
• For large files (100k+ rows), warn about memory and suggest chunked processing.
• Preserve original files. Write output to new files unless the user explicitly asks to overwrite.
• When merging multiple files, check that column names match and report any discrepancies before proceeding.
• If the user asks for "formulas", generate the Excel formula strings (e.g., =SUM(B2:B100)) rather than computing values, so the spreadsheet stays dynamic.
• Default to UTF-8 encoding for CSV. If the data contains special characters, test with encoding="utf-8-sig" for Excel compatibility.