The Scenario
You own the shop and you have a nagging feeling that your technicians are logging time but not all of it is getting billed. Someone mentioned last week that a few timers get marked non-billable by default and nobody is reviewing them. You want to pull the last 30 days of timer entries — technician, ticket, hours, and charged status — into a Google Sheet and see what the ratio actually looks like. This isn't a formal audit. It's a gut check that requires real data.
The bad version:
- Navigate to the Timers section in RepairShopr. Export to CSV. Open the CSV.
- Find that "charged" exported as a boolean integer — 0 or 1 — not the words "yes" or "no" that you'd actually want to read.
- Build a formula to convert 0s and 1s to labels. Sort by technician. Realize the sort is mixing up technician names because two employees share a first name and the export only included the first name.
You wanted a gut check. Instead you have a data cleanup project.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Google Sheet. It reads the sheet, understands what you're analyzing, and through its built-in RepairShopr integration it can pull all ticket timers from the past 30 days with readable field values — charged status as a label, full technician name, no formula post-processing required.
Fetch all RepairShopr ticket timers from the last 30 days and write technician name, ticket ID, hours logged, and charged status into columns A through D
What You Get
- Column A: technician full name
- Column B: ticket ID
- Column C: hours logged (numeric)
- Column D: charged status as "Charged" or "Not Charged" (not 0/1)
- Timer entries with zero hours are included, not excluded — so you can see gaps
What If the Data Is Not Quite Ready
You want to see the ticket subject alongside the timer for context
Export all timer entries from RepairShopr into this sheet with ticket number, ticket subject, user name, time spent, and whether the timer was charged
You want to subtotal hours by technician without a pivot table
Fetch all RepairShopr ticket timers from the last 30 days and write technician name, ticket ID, hours, and charged status into columns A through D; then add a summary table starting in column F showing total hours and billable hours per technician
You only want to see non-billable entries to investigate the pattern
Pull all RepairShopr ticket timers from the last 30 days where charged status is false and write technician name, ticket ID, hours logged, and ticket subject into columns A through D
Full labor audit in one pass: all timers, flag unbilled over threshold, subtotal by tech
Fetch all RepairShopr ticket timers from the last 30 days and write technician name, ticket ID, hours, and charged status into columns A through D; put "REVIEW" in column E for any non-charged entry where hours is greater than 1; add a subtotal section in column G showing total hours, billable hours, and unbilled hours per technician
The pattern: one prompt gets you the analysis-ready view of the labor data — the gut check becomes a real answer instead of a forty-five-minute spreadsheet project.
Try It
Open a Google Sheet and get the 7-day free trial of SheetXAI — ask it to pull last month's RepairShopr timer entries, flag non-billable work over an hour, and subtotal by technician so you can see where the billable hours are going. For related work, see how to export open tickets for workload planning or the RepairShopr integration overview.