An end-to-end automation skill that transforms raw receipt images and PDFs into organized, submission-ready expense claim packages.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install tcs-expense-claim
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 tcs-expense-claim 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 Expense Claim Processor?
The Expense Claim skill is a comprehensive workflow designed for Openclaw Skills to handle the complexity of business travel reimbursements. It leverages vision and text parsing to ingest receipts, invoices, and bills, automatically extracting critical data points like vendor, date, amount, and tax. By mapping expenses to standardized corporate categories and handling multi-currency conversions, it eliminates the manual effort usually required for filing claims in systems like SAP Concur or iExpense.
This skill is particularly valuable for professionals who need to manage high volumes of travel documentation. It ensures that every transaction is categorized correctly, from hotel folios to local conveyance, while providing a clear audit trail through generated PDFs and color-coded spreadsheets.
Expense Claim Processor Use Cases
- Preparing a consolidated expense report after an international business trip.
- Batch processing multiple receipt images and PDF invoices into a single structured Excel file.
- Converting foreign currency expenses into a base currency like USD or INR for corporate reimbursement.
- Generating daily spend summaries and category-wise breakdowns for project budget tracking.
How Expense Claim Processor Works
- Scans the upload directory to ingest and parse all receipt images and PDFs using vision and text extraction.
- Classifies each transaction into specific expense categories like Travel, Meals, or Accommodation based on vendor and item data.
- Generates standardized PDF files named by date and category, merging multiple receipts from the same day into single documents.
- Compiles a multi-sheet XLSX workbook featuring color-coded categories, original amounts, and converted currency totals.
- Presents structured summary tables in the chat interface for quick manual entry into corporate expense portals.
Expense Claim Processor Setup
To use this within your environment for Openclaw Skills, ensure the required Python libraries are installed for PDF and Excel processing:
pip install pypdf img2pdf Pillow pandas openpyxl
Upload your receipt files to the /mnt/user-data/uploads/ directory and initiate the workflow by asking the agent to "process my travel bills" or "organize my receipts."
Expense Claim Processor Data Schema & Taxonomy
The skill maps all extracted data to a strict schema to ensure compatibility with reporting tools:
| Field | Format | Description |
|---|---|---|
| date | DD-Mon-YY | The transaction date as per the receipt |
| vendor | string | The name of the merchant or service provider |
| category | enum | Pre-defined categories (e.g., Conveyance, Meal, Hotel) |
| currency | string | ISO currency code (USD, EUR, INR, etc.) |
| amount_orig | float | The amount printed on the original bill |
| bill_no | string | The receipt or invoice number |
| notes | string | Brief description or policy flags |
Expense Claim Processor Advanced Features
- Intelligent multi-currency conversion with live or reference-based exchange rate lookups.
- Automated platform fee bundling for food delivery services like Zomato or Swiggy.
- Smart flagging of policy-sensitive items like gifts or ambiguous retail purchases.
- Support for merging same-day, same-category receipts into a single PDF for cleaner documentation.
- Dual-sheet Excel generation including a detailed ledger and a high-level category summary.
SKILL.md
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