Solar Utility Meter Identifier Conflict

Find where your photos and application fields disagree.

A downloadable skill for your AI agent.

Your result

Conflicting meter identifiers.

Source references, missing information, and questions to review stay with the findings.

How to use it

  1. 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.

  2. Provide your records

    Photos and application fields. Confirm the case and period you want to check.

  3. Ask for the report

    Copy the request below into your agent. Add the files and any task details it needs.

  4. 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 Solar Utility Meter Identifier Conflict skill. Review the attached records: photos and application fields. Prepare this result: conflicting meter identifiers. 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 puts records for the same item side by side. It keeps both original values when they differ, so you can check the source.

What you provide

Photos and application fields.

Details it records

  • Photo label
  • Submitted field
  • Source date
Read the full task logic

For this task, keep one review row for each project + meter identifier. Record photo label, submitted field, and source date. Keep the source location beside each value.

Crosscheck identifiers across approved plans, datasheets, utility forms, inspection records, and final handover. Preserve every rejection comment and response.

Use the authority's supplied checklist. A complete packet is not an engineering approval, permit, or permission to operate.

A changed value does not show which source is correct. Semantic equivalence and identity aliases need an explicit mapping and review.

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

Synthetic inputs for Solar Utility Meter Identifier Conflict
Starting recordphoto label: source-A
Record to checkphoto label: source-B

What comes back

A difference needs review

  • The two records give different values for the same item.

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": "project + meter identifier / SYNTHETIC-001", "value": {"photo label": "source-A"}}
  • source-o1 · observed/o1
    {"id": "o1", "key": "project + meter identifier / SYNTHETIC-001", "value": {"photo label": "source-B"}}

Checked values

  • expected: photo label: source-A; observed: photo label: 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-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.

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.

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.
  1. Collect the records

    Start with photos and application fields. Confirm the case, period, and source versions before the review.

  2. Extract the task fields

    Use project address, utility account, permit, equipment model, plan revision to keep records distinct. The agent records the original value, an exact quote, and the source location.

  3. Compare source values

    Match each item, then compare its supplied values. Run the Python helper on the structured records. Check each finding against its source.

  4. Complete the report

    Prepare the task result: conflicting meter identifiers. 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.