Supplier Pack-Size Price Creep Check
Compare the price per item in your invoice history and pack sizes.
Restaurant purchasing
Find possible duplicate entries in your event receipts and restaurant purchases.
A downloadable skill for your AI agent.
Costs assigned to multiple jobs.
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.
Event receipts and restaurant purchases. 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 Catering Purchase Allocation Check skill. Review the attached records: event receipts and restaurant purchases. Prepare this result: costs assigned to multiple jobs. 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 groups records with matching values. You review the proposed groups before deciding whether they describe the same event.
Event receipts and restaurant purchases.
For this task, keep one review row for each receipt line + allocation partition. Record cost signature, assigned event, and assigned restaurant job. 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.
A repeated signature is a candidate duplicate. Legitimate repeat events can have the same content. Do not delete, merge, or accuse a party automatically.
This synthetic example shows the included check in use. Your agent uses its findings to complete your task report.
Synthetic example records
| Sample record | cost signature: signature-A |
|---|---|
| Sample record | cost signature: signature-A |
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": "receipt line + allocation partition / SYNTHETIC-001", "value": {"cost signature": "signature-A"}}{"id": "r2", "key": "receipt line + allocation partition / SYNTHETIC-001", "value": {"cost signature": "signature-A"}}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 event receipts and restaurant purchases. 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.
Group exact matching values and report duplicate candidates. Run the Python helper on the structured records. Check each finding against its source.
Prepare the task result: costs assigned to multiple jobs. 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.