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Semi-Automated Weekly Contractor Timesheet Filling from Phone Logs
Work & OperationsIntermediateProfessional services

Semi-Automated Weekly Contractor Timesheet Filling from Phone Logs

Capture work-focus events on iOS into a CSV, then use a desktop Python script with Copilot to fill weekly timesheets.

Time saved
0h / week
Setup time
Not stated
Running cost
Not stated
Cost range
Free

The problem it solves

“Semi-Automated Weekly Contractor Timesheet Filling from Phone Logs” is an intermediate Work & Operations AI agent workflow from Professional services. It connects GitHub Copilot and Other or custom in 4 steps. Setup takes about Not stated and running costs are about Not stated.

Contractor timesheets have to be filled in every week, and reconstructing what happened from memory is tedious and error-prone. The work is repetitive but not complex, so it is a good candidate for partial automation: log activity passively on the phone, then batch-process it on a desktop at the end of the week.

How it flows
  1. Phone logs work focus
  2. Shortcut writes CSV
  3. Desktop app reads log
  4. Copilot helps build script
  5. Timesheet filled

Step-by-step setup

  1. Set up a work-focus trigger on iOS

    Create an iOS Shortcut automation that fires when the phone's work focus mode turns on or off. Have it append a timestamped entry to a CSV file so each work session is recorded without manual input.

  2. Route the CSV to the desktop

    Store the CSV somewhere the home computer can reach at the end of the week, such as a synced folder or cloud drive. Confirm the file accumulates entries across the week without overwriting earlier rows.

  3. Build the timesheet filler with Copilot

    In VSCode, use GitHub Copilot to scaffold a small Python app that reads the CSV, groups entries into sessions, and writes them into the timesheet format. Iterate on the parsing and formatting logic with Copilot's suggestions until the output matches the required layout.

  4. Run the weekly fill and review

    At the end of each week, open the desktop app, point it at the current CSV, and let it populate the timesheet. Skim the generated entries for gaps or misaligned sessions before submitting.

Example prompts

Generate the timesheet filler script
Write a Python script for a desktop app that reads a CSV of work-session timestamps exported from an iOS Shortcut and converts it into rows for a weekly contractor timesheet. The CSV has columns for [TIMESTAMP COLUMN] and [EVENT COLUMN] with values like focus-on and focus-off. Group consecutive on/off pairs into sessions, compute duration per session, and output a table with date, start time, end time, and hours. Handle unmatched events and overnight sessions gracefully. Print a summary of total hours per day. Keep the code in a single file with a clear entry point and add comments explaining each step.
Draft the iOS Shortcut logic
Describe step by step how to build an iOS Shortcut automation that triggers when the work focus mode changes, captures the current date and time, and appends a row to a CSV file named [CSV FILENAME] with columns for timestamp and event type. Explain how to distinguish focus-on from focus-off events, how to ensure the file is appended rather than overwritten, and how to make the file accessible to a desktop computer at the end of the week. Do not include any account names or personal details.

Frequently asked questions

What does the “Semi-Automated Weekly Contractor Timesheet Filling from Phone Logs” workflow do?

Capture work-focus events on iOS into a CSV, then use a desktop Python script with Copilot to fill weekly timesheets.

Which tools do I need?

You need GitHub Copilot and Other or custom.

How long does it take to set up?

Plan for about Not stated. It is rated intermediate. The guide walks through 4 steps, starting with “Set up a work-focus trigger on iOS”.

How much does it cost to run?

Expect about Not stated in running costs (Free range).

What problem does it solve?

Contractor timesheets have to be filled in every week, and reconstructing what happened from memory is tedious and error-prone. The work is repetitive but not complex, so it is a good candidate for partial automation: log activity passively on the phone, then batch-process it on a desktop at the end of the week.

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