Accessibility Ticket Ownership Drift
Check your audit assignments and issue tracker for missing supporting records.
Accessibility remediation records
Find where your approved transcripts and retest transcripts disagree.
A downloadable skill for your AI agent.
Changed spoken output.
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.
Approved transcripts and retest transcripts. 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 Screen Reader Announcement Revision Diff skill. Review the attached records: approved transcripts and retest transcripts. Prepare this result: changed spoken output. 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.
Approved transcripts and retest transcripts.
For this task, keep one review row for each control + announcement sequence. Record action, exact spoken text, reader version, and cut revision. Keep the source location beside each value.
Link findings to reproducible locations and test evidence. Preserve assistive technology, browser, platform, and criterion versions.
Capture comparable screen-reader announcements for the same route, control, action, browser, and reader version. Preserve exact spoken text and order. Compare the extracted arrays, then explain changes in context. The helper does not operate or emulate a screen reader.
A closed ticket does not prove remediation. Report exact retest evidence and untested states; do not claim full accessibility from a subset.
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 | action: source-A |
|---|---|
| Record to check | action: 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": "control + announcement sequence / SYNTHETIC-001", "value": {"action": "source-A"}}{"id": "o1", "key": "control + announcement sequence / SYNTHETIC-001", "value": {"action": "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 approved transcripts and retest transcripts. Confirm the case, period, and source versions before the review.
Use finding, component, route or state, test environment, criterion version 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: changed spoken output. 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.