Warehouse Cutoff In-Transit Bridge
Put the events in your transfer log and period-end snapshot into date order.
Warehouse exceptions
US$11View skill
Inventory Adjustment Reason Completeness
Check your adjustment ledger for missing supporting records.
A downloadable skill for your AI agent.
Your result
Missing reason and reviewer fields.
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
Adjustment ledger. 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 Inventory Adjustment Reason Completeness skill. Review the attached records: adjustment ledger. Prepare this result: missing reason and reviewer fields. 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 lists the evidence each item needs, then looks for a matching record. Missing records and unclear matches stay in the report.
What you provide
Adjustment ledger.
Details it records
- Reason
- Quantity
- Reviewer
- Approval reference
Read the full task logic
For this task, keep one review row for each adjustment + required field. Record reason, quantity, reviewer, and approval reference. Keep the source location beside each value.
Preserve expected, received, damaged, quarantined, and available quantities separately. Model both endpoints of a transfer and each unit conversion.
Require explicit carton or pallet factors. Keep ownership, location, and disposition distinct. Never correct inventory from an inferred match.
A matching key proves only that a supplied record exists. Inspect its content and required fields before calling the requirement satisfied.
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 | One required supporting record |
|---|---|
| Observed | No records supplied |
What comes back
A difference needs review
- The required item has no matching supporting record.
This result comes from the checking tool in your download.
Read the source records
These are synthetic records from the bundled example.
- source-e1 ยท expected/e1
{"id": "e1", "key": "adjustment + required field / SYNTHETIC-001"}
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 adjustment ledger. Confirm the case, period, and source versions before the review.
Extract the task fields
Use warehouse, SKU, lot or serial, movement ID to keep records distinct. The agent records the original value, an exact quote, and the source location.
Match required evidence
Compare required items with observed evidence using exact keys. Run the Python helper on the structured records. Check each finding against its source.
Complete the report
Prepare the task result: missing reason and reviewer fields. 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.