How to Connect Clockify to Excel (4 Methods Compared)

The Problem With Getting Sheet Data In and Out of Clockify

You have an Excel workbook with project names, client names, hours, and billing rates. You need those entries in Clockify — or you need what's in Clockify pulled back out for an invoice. Neither direction is hard once. Both are exhausting when they happen every week.

Clockify is good at tracking time across projects, users, and clients with minimal friction. But the moment you want that data in a workbook — or want to load a workbook's worth of historical entries into Clockify — you're doing something the product doesn't make easy. The default flow is exporting a CSV from Clockify, opening it in Excel, reformatting the date column, renaming headers, and pivoting the client grouping by hand.

Below are the four ways teams approach this. The last one is the only one that doesn't create a second job.

Method 1: CSV Export and Reformat

Export from Clockify's reports panel, open the CSV in Excel, reformat. Clockify's date fields come out in ISO format — Excel renders them as text strings until you fix the column type. Project names use whatever naming convention whoever configured the workspace used. Client names in the CSV and client names in your rate table are close, but not identical, so VLOOKUP fails on a handful of rows every time.

Then do it again next month.

The mismatch is structural. You can't fix it once — you inherit it on every cycle. Twenty minutes of reconciliation per run, never faster, because the problem is in the gap between how Clockify formats its output and how your workbook expects its input.

Method 2: Power Automate

Power Automate has a Clockify connector. You can wire a trigger on a new time entry and write it to an Excel workbook row. Or create a time entry in Clockify when a new worksheet row is added.

Before you go further — do you know what a flow trigger is? A connector action? How to handle the difference between a duration string like "PT1H30M" and a decimal hours value in a cell? If those feel unfamiliar, skip to Method 3 or 4. This path requires someone who builds flows, not someone who needs a report ready before Monday's call.

If you're still reading: the flow works, but Clockify's API fields are not what Power Automate's connector exposes by default. Workspace ID, project ID, user ID — these are UUIDs you'll need to look up separately. The built-in connector covers basic entry creation but hits a wall the moment you want filtered reports or bulk operations.

A trigger-per-entry flow is not the same as a summary report. If you need last month's hours grouped by project compared against a budget column, that's an API report call — not a trigger event. Power Automate's Clockify connector doesn't run reports. You'd need a custom HTTP action to call the reporting API, parse the JSON, loop the rows, and write them to your workbook. That's a non-trivial build.

You probably just need the hours report with totals. You probably have no idea how to chain a custom HTTP action against Clockify's reporting endpoint, parse the response, and write it into specific columns in your workbook. So the ask lands with whoever on your team understands Power Automate flows, and the invoice waits.

Cost tiers climb as soon as you add steps, scheduled runs, or premium connectors.

Method 3: The Previous Generation — Connector Add-Ons

Until recently, the most practical option for teams who wanted workbook ↔ Clockify workflows was a category of add-ons that let you define a field map, save it, and run it on a schedule. You picked your workspace, selected your worksheet range, mapped columns, and executed.

That was a real step forward. Configs were reusable. Output landed in the same shape every time. You didn't have to rebuild the thing from scratch each month.

But you were still responsible for the map — what columns match which Clockify fields, what date range to pull, what filter to apply. The tool transported the data; the logic for what to transport was still your problem. When Clockify project names changed, the map silently returned wrong data until someone caught it in the numbers.

This is the previous generation. Useful — but it left you holding the thinking.

The Easy Way: Using SheetXAI in Excel

There is a different approach entirely. SheetXAI is an AI agent that lives inside your Excel workbook. It reads the workbook, understands what you're looking at — column headers, client names, rate tables — and through its built-in Clockify integration it can pull reports, create entries, delete entries, and update project settings for you. No field map. No connector configuration. You describe what you want.

Example 1: Pull this month's billable hours by client

Pull a Clockify detailed report for this month filtered to billable entries only. Group by client in the "Invoice Summary" sheet and calculate total billable hours and invoice amount for each client using the rate in column G.

The agent fetches the report, groups the entries, looks up each client's rate in column G, and writes a clean summary row per client into the Invoice Summary sheet.

Example 2: Import a backlog of historical entries

Create a Clockify time entry for each row in my workbook using the project name in column A, description in column B, start time in column C, and end time in column D. Write the returned entry ID into column E, or the error into column F if it fails.

The pattern: you don't manually structure the import and you don't write the API loop. You ask for both the import and the audit trail in one prompt. SheetXAI handles the row-by-row execution and the writeback inline.

Try It

Get the 7-day free trial of SheetXAI and open any Excel workbook with Clockify data or time entry rows, then ask it to run a report or start an import. The Clockify integration is included in every SheetXAI plan.