A high-performance toolkit for extracting structured sales data from report images using OCR and AI-driven JSON parsing.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install sales-report-parser
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 sales-report-parser 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 Sales Report Parser?
The sales-report-parser is a sophisticated integration within the Openclaw Skills ecosystem designed to bridge the gap between physical or image-based sales reports and digital data analysis. By leveraging cnocr for optical character recognition and the MiniMax LLM for intelligent data parsing, it transforms unstructured images into clean, actionable JSON and Excel formats.
This skill is particularly effective for businesses needing to digitize daily sales logs, bakery reports, or retail summaries without manual data entry. It provides a modular approach to data processing, allowing developers to run OCR, LLM extraction, and spreadsheet generation as individual steps or as a single automated pipeline.
Sales Report Parser Use Cases
- Digitizing printed or handwritten daily sales reports from retail stores into structured databases.
- Automating the conversion of bakery sales logs into professional Excel spreadsheets for accounting.
- Batch processing large directories of historical report images for monthly financial auditing.
- Extracting specific metrics like net sales, transaction counts, and product-specific totals for deep data analytics.
How Sales Report Parser Works
- The system accepts a single sales report image or a directory of images as the primary input.
- The OCR engine (cnocr) scans the image to identify and extract raw text segments.
- The raw text is passed to an LLM (MiniMax) using a specialized prompt to parse unstructured text into a structured JSON schema.
- The resulting JSON data is processed by a conversion script that generates a formatted Excel (.xlsx) file.
- Users can utilize the batch extraction script to trigger the full end-to-end lifecycle automatically for multiple files.
Sales Report Parser Setup
To begin using this skill, navigate to the scripts directory and install the required dependencies:
cd /data/sales-report-parser/skills/sales-report-parser/scripts
pip install -r requirements.txt
Run the batch extraction tool by providing your API key and image path:
python batch_extract.py --api_key "YOUR_API_KEY" --image /path/to/images/ --output my_sales_data
Sales Report Parser Data Schema & Taxonomy
The skill organizes extracted data into a structured format compatible with modern analytics tools. The following fields are typically identified and exported:
| Field | Description |
|---|---|
| Date | The calendar date extracted from the report |
| Total Sales | The gross sales amount recorded |
| Product Categories | Itemized sales for categories like bread, cakes, and pastries |
| Transaction Count | The total number of customer transactions |
| Cards/Coupons | Data related to gift cards or promotional usage |
Sales Report Parser Advanced Features
- Multi-stage pipeline architecture allowing for manual intervention or full automation.
- Support for MiniMax API with customizable parameters like temperature and model version.
- Advanced Excel formatting options including table transposition and automated date-based sorting.
- Robust batch processing mode with configurable retry logic for high-volume environments.
- High-accuracy OCR powered by cnocr for localized text recognition.
SKILL.md
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