Auto Parts Supersession Quote Trace
Turn your supplied manufacturer crosswalk and quotes into a review with clear source references.
Auto repair administration
Find where your insurer authorization and repair estimate disagree.
A downloadable skill for your AI agent.
Unresolved approved-scope differences.
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.
Insurer authorization and repair estimate. 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 Repair Insurance Approval Difference skill. Review the attached records: insurer authorization and repair estimate. Prepare this result: unresolved approved-scope differences. 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 puts records for the same item side by side. It keeps both original values when they differ, so you can check the source.
Insurer authorization and repair estimate.
For this task, keep one review row for each repair scope + approved field. Record insurer-approved item, proposed item, and difference. Keep the source location beside each value.
Link estimate revisions, authorizations, parts, cores, insurer replies, labor, and customer payments. Preserve agreed and disputed scope separately.
Use supplied labor rules, warranty requirements, and return windows. Administrative findings do not establish repair safety or coverage.
A changed value does not show which source is correct. Semantic equivalence and identity aliases need an explicit mapping and review.
This synthetic example shows the included check in use. Your agent uses its findings to complete your task report.
Synthetic example records
| Starting record | insurer-approved item: source-A |
|---|---|
| Record to check | insurer-approved item: source-B |
A difference needs review
This result comes from the checking tool in your download.
These are synthetic records from the bundled example.
{"id": "e1", "key": "repair scope + approved field / SYNTHETIC-001", "value": {"insurer-approved item": "source-A"}}{"id": "o1", "key": "repair scope + approved field / SYNTHETIC-001", "value": {"insurer-approved item": "source-B"}}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 insurer authorization and repair estimate. Confirm the case, period, and source versions before the review.
Use vehicle VIN, repair order, estimate revision, part/core ID to keep records distinct. The agent records the original value, an exact quote, and the source location.
Match each item, then compare its supplied values. Run the Python helper on the structured records. Check each finding against its source.
Prepare the task result: unresolved approved-scope differences. 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.