sheetsmith

What This Skill Does

Sheetsmith is a high-efficiency pandas-powered CLI agent designed to streamline spreadsheet manipulation tasks. Instead of requiring users to write custom Python scripts or manually navigate heavy spreadsheet software, Sheetsmith provides an abstraction layer for common data tasks. It handles CSV, TSV, and XLSX/XLS files with equal ease, offering built-in commands for structural inspection, deep statistical description, ad-hoc filtering, and complex column-level transformations. By leveraging the power of the pandas ecosystem under the hood, it ensures that data processing is fast, consistent, and reliable, outputting results directly to the console or writing cleansed files back to disk.

Installation

To integrate this skill into your OpenClaw environment, use the command: clawhub install openclaw/skills/skills/crimsondevil333333/sheetsmith. Ensure you have Python 3 and the necessary pandas dependencies installed in your environment before executing commands.

Use Cases

Sheetsmith is ideal for data analysts, software developers, and researchers. Common use cases include:

• Rapid Data Cleaning: Removing rows with missing values or filtering datasets based on complex boolean conditions without opening Excel.
• Reporting and Summarization: Generating quick diagnostic reports on large files to identify potential data quality issues like unexpected nulls or data type mismatches.
• Format Normalization: Batch converting legacy TSV or messy Excel files into clean, standardized CSV files for downstream pipelines.
• Data Engineering: Quickly creating derived metrics (e.g., unit price * quantity) to test hypotheses or prepare datasets for machine learning ingestion.

Example Prompts

1. "Sheetsmith, summarize the file data/sales_q3.csv to check for missing values in the customer_id column."
2. "Use Sheetsmith to filter database_dump.xlsx for all records where 'region' is 'West' and 'revenue' is greater than 50000, then save the output to west_high_revenue.csv."
3. "Transform the file inventory.csv by creating a new 'total_value' column using 'unit_cost' * 'quantity', then show me a 10-row preview."

Tips & Limitations

• Safety: Always use the --output flag to create a new file unless you are absolutely sure you want to overwrite your source with --inplace.
• Large Files: While pandas is efficient, Sheetsmith loads files into memory. If processing files exceeding several gigabytes, verify your system memory capacity.
• Expressions: When using the transform command, remember that standard pandas syntax applies; avoid using dangerous or non-pandas functions within your --expr strings to ensure safety and stability.
• Flexibility: You can combine flags, such as using --tail with --summary, to quickly verify that recent data imports were successful.