A high-performance Python-based utility for parsing and extracting structured data from multiple Excel file formats.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install excel-parser-skill
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-parser-skill 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 Parser?
The Excel Parser is a robust skill designed for developers and AI agents needing to read, analyze, and convert spreadsheet data into machine-readable formats. Leveraging the high-speed calamine library, it handles legacy .xls files alongside modern .xlsx and .xlsm formats with ease.
This tool is an essential addition to any workflow involving Openclaw Skills where automated data processing, report analysis, or content auditing is required. It bridges the gap between tabular spreadsheet data and structured JSON or text formats, making it ideal for feeding data into LLMs or data pipelines.
Excel Parser Use Cases
- Extracting structured data from financial reports for automated analysis.
- Converting complex Excel workbooks into clean text or JSON for LLM consumption.
- Batch processing legacy spreadsheet archives into modern data pipelines.
- Automating content auditing and validation within large Excel datasets.
How Excel Parser Works
- The user provides a path to an Excel file (.xls, .xlsx, .xlsm, etc.) to the parser module.
- The skill utilizes the calamine engine (or optional backends like openpyxl) to load the workbook into memory efficiently.
- Data is iterated through sheets and rows based on configurable limits such as maximum row counts.
- The skill generates a structured JSON response containing raw cell data, sheet metadata, and a formatted text representation.
- Results are returned to the calling agent or application for downstream processing, reporting, or storage.
Excel Parser Setup
First, ensure your environment meets the Python 3.7+ requirement. Install the necessary core dependencies:
pip install python-dotenv python-calamine
For legacy .xls support or specific .xlsx features, you may install optional dependencies:
pip install xlrd openpyxl
Configure your .env file to manage processing limits and formatting behavior:
# Limit the number of rows to process (default 100)
EXCEL_MAX_ROWS=100
# Toggle whether to preserve empty rows
EXCEL_KEEP_EMPTY_ROWS=false
Excel Parser Data Schema & Taxonomy
The Excel Parser returns a detailed object structure designed for easy integration:
| Field | Type | Description |
|---|---|---|
| text | string | A formatted text representation of all spreadsheet content. |
| sheets | array | A list of objects containing sheet names, row data, and dimensions. |
| sheet_count | integer | Total number of sheets processed in the workbook. |
| total_cells | integer | Total count of cells identified across all sheets. |
| engine | string | The underlying parsing engine used for the operation. |
Excel Parser Advanced Features
- Supports legacy Excel 97-2003 formats (.xls) via optional xlrd integration.
- High-performance parsing of modern macro-enabled workbooks (.xlsm) and templates (.xltx/ .xltm).
- Configurable row limits to prevent memory overflow during large file processing within Openclaw Skills.
- Flexible text output generation optimized for direct input into Large Language Models (LLMs).
- Dual-mode operation supporting both object-oriented (ExcelParser class) and functional (process_excel) patterns.
SKILL.md
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