Amenity Outage Message Consistency
Find where your maintenance notes and guest drafts disagree.
Short-term rental operations
Turn your cancellation export and cleaning roster into a review with clear source references.
A downloadable skill for your AI agent.
Affected visits for review.
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.
Cancellation export and cleaning roster. 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 Same-Day Cancellation Turnover Replan skill. Review the attached records: cancellation export and cleaning roster. Prepare this result: affected visits for review. 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 keeps recorded facts, assumptions, and unanswered questions separate. Each item retains the evidence a reviewer needs to make a decision.
Cancellation export and cleaning roster.
For this task, keep one review row for each stay + cancelled turnover. Record cancellation time, roster status, affected visits, and unresolved assignment. Keep the source location beside each value.
Separate guest promises from completed service. Join booking dates to the correct property and turnover. Retain exact image hashes when checking reuse.
Account for local time zones, laundry turnaround, access windows, and same-day occupancy. Do not infer cleaning quality from file presence.
This method checks evidence references only. Interpretation, semantic matching, causal analysis, or synthetic test design is performed by the agent and must be reviewed. No model evaluation is implied by a passing fixture test.
This synthetic example shows the included check in use. Your agent uses its findings to complete your task report.
Synthetic example records
| Sample record | claim: Synthetic observation about cancellation time; classification: inferred; question: What evidence can confirm this inference? |
|---|
A reviewer must decide
This result comes from the checking tool in your download.
These are synthetic records from the bundled example.
{"claim": "Synthetic observation about cancellation time", "classification": "inferred", "id": "r1", "key": "stay + cancelled turnover / SYNTHETIC-001", "question": "What evidence can confirm this inference?"}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 cancellation export and cleaning roster. Confirm the case, period, and source versions before the review.
Use property, stay, turnover, room or task to keep records distinct. The agent records the original value, an exact quote, and the source location.
Keep each claim, its classification, and the question that needs a reviewer. Run the Python helper on the structured records. Check each finding against its source.
Prepare the task result: affected visits for review. 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.