Restaurant Free-Goods Deal Check
Check your supplier offer and invoices for missing supporting records.
Restaurant purchasing
Check whether the amounts in your prep counts waste log and sales agree.
A downloadable skill for your AI agent.
Unexplained quantity variance.
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.
Prep counts waste log and sales. 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 Waste Log Prep-Sheet Reconciliation skill. Review the attached records: prep counts waste log and sales. Prepare this result: unexplained quantity variance. 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 adds the amounts for each item and compares the total with your starting figure. The report shows the difference and its source records.
Prep counts waste log and sales.
For this task, keep one review row for each product + production period. Record prep quantity, sales, waste, and closing stock. 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 cannot establish that all transactions were exported. It does not perform currency conversion, infer missing balances, or post adjustments.
This synthetic example shows the included check in use. Your agent uses its findings to complete your task report.
Synthetic example records
| Starting record | amount: 100.00; unit: example units |
|---|---|
| Record to check | amount: 125.00; unit: example units |
A difference needs review
This result comes from the checking tool in your download.
These are synthetic records from the bundled example.
{"amount": "100.00", "id": "e1", "key": "product + production period / SYNTHETIC-001", "unit": "synthetic-unit"}{"amount": "125.00", "id": "o1", "key": "product + production period / SYNTHETIC-001", "unit": "synthetic-unit"}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 prep counts waste log and sales. 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.
Add the observed amounts and compare the total with the supplied baseline. Run the Python helper on the structured records. Check each finding against its source.
Prepare the task result: unexplained quantity variance. 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.