ChatGPTBeginner7 min readUpdated May 15, 2025

ChatGPT Weekly Review Agent

A repeatable prompt-chain that turns your calendar, task list, and journal notes into a Friday review with wins, blockers, and next-week priorities.

chatgptproductivityreviewprompt-chain

Download this template

Grab a structured copy of "ChatGPT Weekly Review Agent" as JSON (for programmatic import) or Markdown (for docs and README files). Both are licensed CC-BY-4.0 with attribution.

Overview

A weekly review is the highest-leverage 30 minutes in a knowledge worker's week. This prompt-chain gives ChatGPT the raw material — completed events, tasks, and free-form notes — and returns a structured review you can actually act on.

How it works

  1. You paste three inputs: last week's completed calendar events, closed tasks, and any notes.
  2. Stage 1 prompt extracts wins, blockers, and unresolved threads.
  3. Stage 2 prompt asks 'what would a great manager coach you on?' and returns 3 questions.
  4. Stage 3 prompt drafts a 5-item priority list for next week with time estimates.

Benefits

  • Turns fragmented data into a single narrative you can share with a manager.
  • Coaching questions force self-reflection without hiring a coach.
  • Time-estimated priorities calibrate what's realistic before Monday.

Use cases

  • IC engineers preparing weekly updates.
  • Founders doing solo Friday retros.
  • Managers preparing 1:1s with themselves before their skip-level.

Step-by-step guide

Step 1: Export your week

Copy completed events from Calendar, closed tasks from your task manager, and any notes from Notes/Notion.

Step 2: Run stage 1

Paste inputs with the extraction prompt. Ask for a table: category, item, outcome, effort.

Step 3: Run stage 2

Feed stage 1 output back and ask for coaching questions. Answer them in your own words before continuing.

Step 4: Run stage 3

Ask for next-week priorities based on stage 1 + your answers. Cap at 5 with time estimates that sum to ~60% of your available focus time.

Step 5: Save the output

Store the review in a running doc. Over months it becomes a career-review dataset.

FAQs

Related resources

Workflows, articles, and tools that pair with this build.