Expense Categorizer

Expense Categorizer Skill

Auto-categorize expenses from receipts using ML/rule-based categorization with learning from user corrections.

Features

• Smart Categorization: Rule-based and ML-inspired categorization using merchant names, keywords, and receipt content
• Custom Categories: Define your own expense categories beyond the defaults
• Learning System: Improves accuracy by learning from user corrections
• Bulk Categorization: Process multiple receipts at once
• Confidence Scoring: Each categorization includes a confidence score
• Merchant Mapping: Automatic merchant-to-category mapping based on history

Installation

cd skills/expense-categorizer
npm install
npm run build

Usage

CLI

# Check status
npm run cli -- status

# Categorize a single receipt
npm run cli -- categorize 1

# Categorize all uncategorized receipts
npm run cli -- bulk

# Suggest category for a receipt (dry run)
npm run cli -- suggest 1

# Apply a correction (teaches the system)
npm run cli -- correct 1 --category "Office Supplies"

# Manage categories
npm run cli -- categories
npm run cli -- add-category "Professional Development" --keywords "course,training,certification,conference"

# View merchant mappings
npm run cli -- merchants
npm run cli -- map-merchant "Starbucks" --category "Dining"

# Statistics
npm run cli -- stats
npm run cli -- accuracy

Library

import { ExpenseCategorizerSkill } from '@openclaw/expense-categorizer';

const skill = new ExpenseCategorizerSkill();

// Categorize a single receipt
const result = await skill.categorizeReceipt(1);
console.log(result.category);  // "Dining"
console.log(result.confidence);  // 0.92

// Bulk categorize all uncategorized receipts
const results = await skill.bulkCategorize();

// Suggest category without applying
const suggestion = await skill.suggestCategory(1);

// Apply correction to teach the system
await skill.applyCorrection(1, 'Office Supplies');

// Manage custom categories
await skill.addCategory({
  name: 'Professional Development',
  keywords: ['course', 'training', 'certification'],
  parentCategory: 'Business'
});

// Get merchant mappings
const mappings = await skill.getMerchantMappings();

// Manually map a merchant
await skill.mapMerchant('Starbucks', 'Dining');

// Get categorization statistics
const stats = await skill.getStats();

await skill.close();

Default Categories

• Groceries: Supermarkets, food stores
• Dining: Restaurants, cafes, fast food
• Gas: Fuel stations, car services
• Pharmacy: Drugstores, medical supplies
• Office: Office supplies, stationery
• Electronics: Tech stores, gadgets
• Transportation: Rideshare, transit, parking
• Entertainment: Movies, games, events
• Retail: General shopping
• Utilities: Electricity, gas, water, internet
• Travel: Hotels, flights, car rentals
• Healthcare: Medical, dental, vision

How It Works

1. Rule-Based Matching: First checks merchant name against known mappings
2. Keyword Analysis: Analyzes receipt content for category keywords
3. Line Item Analysis: Considers purchased items for categorization
4. Learning: When you correct a category, the system learns for future receipts

Data Storage

Data is stored in ~/.openclaw/skills/expense-categorizer/:

• categorizer.db - SQLite database with categories, mappings, and learning data

Confidence Levels

• High (80-100%): Strong merchant match or multiple keyword matches
• Medium (50-79%): Partial keyword matches or similar merchant
• Low (<50%): No clear match - manual review recommended