Lab Sample Parent-Child Lineage
Trace the links between items in your aliquot records and parent register.
Research data administration
US$11View skill
Research Dataset Column Meaning Drift
See what changed in your data dictionary versions and tables.
A downloadable skill for your AI agent.
Your result
Changed or undocumented column meanings.
Source references, missing information, and questions to review stay with the findings.
How to use it
Add the skill to your AI
Open your download and follow the start guide. Use an AI agent that can load skill files and run the included tools.
Provide your records
Data dictionary versions and tables. Confirm the case and period you want to check.
Ask for the report
Copy the request below into your agent. Add the files and any task details it needs.
Review and save
Check the findings against the original records. Choose where to save the report and follow up on open questions.
Example request
Use the Research Dataset Column Meaning Drift skill. Review the attached records: data dictionary versions and tables. Prepare this result: changed or undocumented column meanings. 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.
What it checks
It compares the earlier and later records. It separates added, removed, and changed items and keeps a reference to each source.
What you provide
Data dictionary versions and tables.
Details it records
- Prior meaning
- Revised meaning
- Unit
- Missing definition
Read the full task logic
For this task, keep one review row for each column + dictionary revision. Record prior meaning, revised meaning, unit, and missing definition. Keep the source location beside each value.
Maintain sample identity, lineage, transfers, reagent lots, and data dictionary versions. Preserve blinded identifiers without inferring hidden identities.
Do not infer consent scope or recover missing measurements. Treat frozen dataset changes as evidence requiring researcher review.
A change can be intentional. The engine does not determine approval, business impact, or which version should be used.
Example result
This synthetic example shows the included check in use. Your agent uses its findings to complete your task report.
What you provide
Synthetic example records
| Starting record | prior meaning: source-A |
|---|---|
| Record to check | prior meaning: source-B |
What comes back
A difference needs review
- The value for this item changed between the two versions.
This result comes from the checking tool in your download.
Read the source records
These are synthetic records from the bundled example.
- source-e1 · expected/e1
{"id": "e1", "key": "column + dictionary revision / SYNTHETIC-001", "value": {"prior meaning": "source-A"}} - source-o1 · observed/o1
{"id": "o1", "key": "column + dictionary revision / SYNTHETIC-001", "value": {"prior meaning": "source-B"}}
Checked values
- expected: prior meaning: source-A; observed: prior meaning: source-B
Run this example
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-resultExpected 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.
What is saved
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.
- A readable check report
- The local checking tool can save its findings as report.md.
- A data copy
- The same check can be saved as report.json for use in other tools. A local audit database is optional.
Tonotes does not receive your work documents through this website. Documents you share with an AI service follow that service's storage settings.
Your download
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.
- A start guide to help you set up the skill.
- Task instructions that tell your AI what to check.
- Checking tools with worked examples.
- A report template with space for evidence and next actions.
Setup and file details
Use an agent that supports SKILL.md instructions and Python 3.10 or later with SQLite. Your AI-agent service is separate.
- Start guide: 1 file
- START-HERE.md explains setup and the expected example result.
- Agent instructions: 1 file
- SKILL.md defines this task and its completion checks.
- Task references: 4 files
- The task guide, input contract, tools guide, and research notes.
- Python tools: 4 files
- Check records, convert CSV input, and run the local checks.
- Examples and templates: 8 files
- The input schema, synthetic cases, expected report, and report template.
Collect the records
Start with data dictionary versions and tables. Confirm the case, period, and source versions before the review.
Extract the task fields
Use study, sample, aliquot, run, location, protocol version to keep records distinct. The agent records the original value, an exact quote, and the source location.
Review changed records
Compare the baseline with the later version using exact keys. Run the Python helper on the structured records. Check each finding against its source.
Complete the report
Prepare the task result: changed or undocumented column meanings. 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.