Restaurant Credit Memo Receipt Match
Check whether the amounts in your disputed deliveries and credit memos agree.
Restaurant purchasing
Compare the price per item in your invoice history and pack sizes.
A downloadable skill for your AI agent.
Normalized unit-price changes.
Source references, missing information, and questions to review stay with the findings.
Open your download and follow the start guide. Use an AI agent that can load skill files and run the included tools.
Invoice history and pack sizes. Confirm the case and period you want to check.
Copy the request below into your agent. Add the files and any task details it needs.
Check the findings against the original records. Choose where to save the report and follow up on open questions.
Use the Supplier Pack-Size Price Creep Check skill. Review the attached records: invoice history and pack sizes. Prepare this result: normalized unit-price changes. Include a source reference for each finding. List missing information and questions that need my review. Ask me to confirm the scope and where to save the report before you start.
It divides each price by the quantity supplied. This lets you compare the price per item even when pack sizes differ.
Invoice history and pack sizes.
For this task, keep one review row for each supplier + product + base unit. Record invoice total, pack size, base quantity, and reference unit price. Keep the source location beside each value.
Normalize purchase quantities using sourced pack size and units. Keep price, credit, free goods, substitution, waste, and inter-location transfer evidence distinct.
Compare like-for-like products and dates. A different pack can hide a unit-price increase. Use approved ingredient and food-handling records.
The engine does not infer pack conversions or currency exchange. A price difference is not necessarily an invoice error.
This synthetic example shows the included check in use. Your agent uses its findings to complete your task report.
Synthetic example records
| Sample record | reference total: 24.00; reference quantity: 12; observed total: 15.00; observed quantity: 6; unit: synthetic-currency/item |
|---|
A difference needs review
This result comes from the checking tool in your download.
These are synthetic records from the bundled example.
{"id": "r1", "key": "supplier + product + base unit / SYNTHETIC-001", "observed_quantity": "6", "observed_total": "15.00", "reference_quantity": "12", "reference_total": "24.00", "unit": "synthetic-currency/item"}After purchase, extract the ZIP. Run these commands from the skill folder. Use a new output folder.
python3 scripts/checks.py
python3 scripts/audit.py assets/example.json \
--files-root assets/sample-files --output-dir example-result
Expected exit code: 1. Code 0 means no helper finding. Code 1 means a finding or review item. Code 2 means incomplete or invalid input.
The check uses the records you supply. Review each finding against the original documents.
Ask your agent to save the completed task report in a folder you choose. The report template includes the result, source references, missing information, and next actions.
Tonotes does not receive your work documents through this website. Documents you share with an AI service follow that service's storage settings.
US$11 includes one skill in a ZIP with 18 files. You receive the instructions and tools to run the task with your own records.
Use an agent that supports SKILL.md instructions and Python 3.10 or later with SQLite. Your AI-agent service is separate.
Start with invoice history and pack sizes. Confirm the case, period, and source versions before the review.
Use supplier, product, pack specification, invoice line, location to keep records distinct. The agent records the original value, an exact quote, and the source location.
Divide each total by its quantity, then compare the two unit prices. Run the Python helper on the structured records. Check each finding against its source.
Prepare the task result: normalized unit-price changes. Include missing evidence, unresolved questions, and the next action for each open item.
Eight synthetic cases check the tool locally. Live-agent accuracy on real documents has not been tested.