Back-to-Back Stay Buffer Check
Find overlapping periods in your bookings and cleaning durations.
Short-term rental operations
Find possible duplicate entries in your cleaner photo files across stays.
A downloadable skill for your AI agent.
Repeated file hashes with conflicting stay dates.
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.
Cleaner photo files across stays. 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 Turnover Photo Reuse Evidence Check skill. Review the attached records: cleaner photo files across stays. Prepare this result: repeated file hashes with conflicting stay dates. 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.
Cleaner photo files across stays.
For this task, keep one review row for each property + image hash partition. Record image hash, stay ID, capture date, and reuse explanation. 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.
Hash the original cleaner image files. Compare hashes across distinct stay IDs and capture dates. Keep repeat use that the host authorized as a separate outcome. Similar-looking images require visual review; a byte hash finds exact reuse only.
Account for local time zones, laundry turnaround, access windows, and same-day occupancy. Do not infer cleaning quality from file presence.
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 | image hash: signature-A |
|---|---|
| Sample record | image hash: 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": "property + image hash partition / SYNTHETIC-001", "value": {"image hash": "signature-A"}}{"id": "r2", "key": "property + image hash partition / SYNTHETIC-001", "value": {"image hash": "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 cleaner photo files across stays. 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.
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: repeated file hashes with conflicting stay dates. 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.