simple-excel

What This Skill Does

The simple-excel skill for OpenClaw is a lightweight, powerful utility designed to streamline spreadsheet management. By leveraging the industry-standard pandas library, it allows users to perform complex data manipulation on .xlsx and .csv files through simple, intuitive commands. Whether you need to automate report generation, clean up raw data, perform mathematical transformations, or merge multiple datasets, this tool handles the heavy lifting. It bridges the gap between raw file storage and analytical insight, providing a structured environment where you can execute operations like filtering, grouping, aggregation, and renaming columns without opening a heavy GUI application.

Installation

To integrate this skill into your environment, use the OpenClaw command-line interface. Run the following command in your terminal:

clawhub install openclaw/skills/skills/anmu14641/simple-excel

Ensure that you have the pandas and openpyxl dependencies installed in your current Python environment to ensure full compatibility with modern Excel file formats.

Use Cases

• Automated Reporting: Quickly consolidate weekly data from multiple CSV files into a formatted Excel report.
• Data Cleaning: Automatically replace missing values, remove duplicates, and reformat headers across large datasets.
• Quick Calculations: Add derived columns, calculate totals, or perform group-based statistics like calculating the average spend per department.
• Data Transformation: Convert datasets between file formats (e.g., CSV to XLSX) for client distribution.

Example Prompts

1. "Open 'sales_data.csv', calculate the total sum of the 'Revenue' column, and save the result to a new file named 'summary.xlsx'."
2. "Read 'inventory.xlsx', filter out all items where the 'Stock' is less than 5, and export the remaining rows to a file named 'restock_list.csv'."
3. "Take the 'user_feedback.xlsx' file, fill all empty cells in the 'Rating' column with a 0, and rename the 'Feedback_Text' column to 'Comments'."

Tips & Limitations

• Performance: While great for small to medium-sized files, if you are processing multi-gigabyte datasets, consider using CSVs instead of XLSX files for faster read/write speeds.
• Exporting: Always use the index=False parameter when saving to files to ensure your Excel sheet doesn't contain an unnecessary auto-incrementing index column.
• Structure: Keep your Excel sheets formatted cleanly; avoid merged cells or complex styling within the spreadsheets, as this library focuses on raw data processing rather than formatting or cell styling. The skill is optimized for structured, tabular data.