Processes Coles grocery invoices to extract structured data and predict future orders. Use when user uploads/pastes invoice content, asks to analyze grocery purchases, or wants shopping predictions.
Scanned 2/12/2026
Install via CLI
openskills install majiayu000/claude-skill-registry---
name: coles-invoice-processor
description: Processes Coles grocery invoices to extract structured data and predict future orders. Use when user uploads/pastes invoice content, asks to analyze grocery purchases, or wants shopping predictions.
---
# Coles Invoice Processor Skill
Analyze Coles grocery store invoices using Python scripts to convert PDFs, extract structured data, and predict future orders with budget forecasts.
## When to Use This Skill
Activate when the user:
- Uploads Coles invoice PDFs or images
- Pastes invoice text content
- Asks to extract grocery item data
- Wants to analyze shopping history
- Requests future order predictions
- Needs shopping budget estimates
## Setup Requirements
Before using the scripts, ensure dependencies are installed:
```bash
# Create virtual environment (optional but recommended)
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
```
**Required packages:** `pymupdf4llm`, `pandas`, `prophet`
## Pipeline Overview
The processing pipeline consists of 3 main scripts:
1. **01_convert.py** - Convert PDFs to Markdown
2. **03_extract_data.py** - Extract structured data from Markdown
3. **04_predict_orders.py** - Predict future orders and budget
## How to Process Invoices
### Step 1: Place Invoice PDFs
Place Coles invoice PDFs in the `input_invoices/` directory.
### Step 2: Convert PDFs to Markdown
```bash
python 01_convert.py
```
This converts each PDF in `input_invoices/` to a Markdown file in the same folder using `pymupdf4llm`.
### Step 3: Extract Structured Data
```bash
python 03_extract_data.py
```
Parses the Markdown invoices and extracts:
- Invoice metadata (number, date, time)
- Categories and items
- Product names, quantities, prices, weights
Output: `output_extracted/extracted_data.json`
### Step 4: Predict Future Orders
```bash
python 04_predict_orders.py
```
Analyzes purchase history and:
- Calculates average purchase intervals per product
- Determines typical quantities
- Forecasts ~150 days of future orders
- Groups orders within 3 days
- Merges small orders (<$50) with adjacent orders within 6 days
- Generates monthly budget estimates
## Data Extraction Details
The extraction script parses Markdown looking for:
**Invoice Metadata:**
- Invoice number: `**Invoice number:** #123456`
- Invoice date: `**Invoice date:** 7 December 2024`
- Invoice time: `**Invoice time:** 14:30:00`
**Product Categories:**
Categories appear as bold headers (e.g., `**Dairy**`, `**Bakery**`, `**Meat & Seafood**`)
**Product Line Items:**
Format: `[Product Name](link) Ordered Picked UnitPrice TotalPrice`
Example:
```
[Coles Full Cream Milk 3L](https://...) 2 2 $4.65 $9.30
```
Extracted fields:
- Product name (including weight/size from name like "3L", "500g", "1kg")
- Quantity ordered
- Quantity picked
- Unit price
- Total price
## Output Formats
### Extracted Data JSON Schema
```json
{
"filename": "ea[REDACTED]_044712.md",
"invoice_number": "[REDACTED]",
"invoice_date": "7 December 2024",
"invoice_time": "14:30:00",
"categories": [
{
"name": "Dairy",
"items": [
{
"product": "Coles Full Cream Milk 3L",
"weight": "3L",
"link": "https://...",
"ordered": "2",
"picked": "2",
"unit_price": "$4.65",
"total_price": "$9.30"
}
]
}
]
}
```
### Predicted Orders Output
```
Order #1 - Approx Date: 2025-12-15 - Total Est. Cost: $95.50
Product | Qty | Unit $ | Total $
--------------------------------------------------------------------------------
Coles Full Cream Milk 3L... | 2 | $4.65 | $9.30
```
### Monthly Budget Output
```
--- Estimated Monthly Budget ---
2025-December: $785.80
2026-January: $738.55
2026-February: $692.40
```
## Privacy Notes
- Invoice numbers are automatically redacted in filenames and output
- Filenames like `ea12345_67890.md` become `ea[REDACTED]_67890.md`
- Sensitive personal information should be manually reviewed
- Focus on product and pricing data only
## Common Categories in Coles Invoices
- Dairy
- Bakery
- Meat & Seafood
- Fruit & Vegetables
- Pantry
- Frozen
- Drinks
- Health & Beauty
- Baby
- Household
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