A Pandas-powered utility for automated Excel data deduplication, missing value imputation, and standardized format conversion.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install excel-clean
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install excel-clean using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Excel Cleaner?
Excel Cleaner is a robust data processing tool designed for the Openclaw Skills ecosystem. It leverages the power of the Pandas library to handle common spreadsheet maintenance tasks that typically require manual intervention. The skill focuses on three primary pillars of data hygiene: removing redundant entries, filling in gaps in datasets, and ensuring that data types—such as dates and numbers—remain consistent across the entire workbook.
By automating these repetitive workflows, Excel Cleaner serves as a bridge between raw, messy data collection and high-quality analysis. It is built with a safety-first approach, generating a new cleaned version of the file while leaving the original source data untouched, ensuring a non-destructive workflow for developers and data analysts alike.
Excel Cleaner Use Cases
- Customer List Deduplication: Identify and remove duplicate customer records or order IDs based on specific column combinations.
- Data Completeness: Automatically fill missing cells with default values, zeros, or placeholders like 'Unknown' to prepare for analysis.
- Standardization: Convert erratic date formats or mixed text/numeric columns into a unified format like YYYY-MM-DD.
- Batch Preparation: Quickly clean large datasets before importing them into databases or visualization tools.
How Excel Cleaner Works
- The skill receives the target file path and optional configuration parameters like columns for deduplication or specific fill values.
- It utilizes the Pandas and Openpyxl libraries to load the Excel sheet into a memory-efficient DataFrame.
- Logic is applied to identify and drop duplicate rows based on the user-defined
dedup_cols. - Missing values are imputed using the specified
fill_valuestrategy (fixed, forward fill, or backward fill). - Type conversion logic standardizes columns (e.g., forcing a column to be strictly datetime or numeric).
- The processed data is exported to a new file appended with
_cleaned.xlsx, and a summary report including final row counts is returned.
Excel Cleaner Setup
To use this within the Openclaw Skills environment, ensure you have the required Python dependencies installed:
pip install pandas>=1.3.0 openpyxl>=3.0.0
Once dependencies are met, the skill can be invoked via your AI agent by passing the file path and processing parameters.
Excel Cleaner Data Schema & Taxonomy
The skill processes .xlsx files and outputs a structured response and a physical file.
| Parameter | Description | Type |
|---|---|---|
file_path |
Absolute path to the source Excel file | String |
dedup_cols |
List of columns to check for uniqueness | Array |
fill_value |
Content used to replace NaN/null values | String/Number |
convert_type |
Targeted format (e.g., 'Date:YYYY-MM-DD') | String |
Output Metadata:
success: Boolean status of the operation.output: Path to the generated_cleaned.xlsxfile.rows: The total count of records remaining after cleaning.
Excel Cleaner Advanced Features
- Non-Destructive Processing: Always preserves original data by creating a separate output file.
- Multi-Column Logic: Supports complex deduplication where uniqueness is defined by a combination of multiple fields.
- Custom Format Mapping: Flexible type conversion that handles the nuances of Excel date-time serialization.
- Openclaw Skills Integration: Designed to be called programmatically by AI agents to perform pre-analysis data scrubbing.
SKILL.md
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