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TeamWork Task Lists per Project

Note: The video covers material not in the guide below — please watch in full.

Action Step

Complete this before moving on.

Open VS Code, navigate to the Playbooks Library, and pick a project — lead routing works as an example. Open the Teamwork Template file inside it and look at how the project is broken down by milestone, task list, and individual tasks. Then create a folder in your personal repo for a hypothetical customer project (e.g., "BrightLoop Lead Routing Project"), copy the Teamwork template into it, and use Claude Code to generate a mock discovery call transcript for that project. Open a second agent session and prompt it to duplicate the template and create a customized version of the task list based on that transcript. Push your limits — try this with a couple of projects you are less familiar with, and if you want to go above and beyond, try pushing milestones or tasks into your test Teamwork project via the API.

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Finding the Teamwork Templates​

Inside the Playbooks Library repo, every one of the 60+ projects contains a Teamwork Template file alongside the playbook folder. Open any project — say, Lead Routing — and you will see the template sitting right there. When you open it and look at the table of contents, you can see the entire project broken down by milestone, task list, and the individual tasks within each task list.

Each task includes a description and an estimated time. These templates were built from the corresponding implementation playbooks, so they mirror the four-phase structure you have already learned about. You do not need to create tasks from scratch when kicking off an engagement — they are already built out for you.

Customizing the Template for a Real Engagement​

The templates are the cookie-cutter version of the task list. When you kick off an actual customer project, you will likely need to customize it rather than pushing it verbatim. The template was created based on the implementation playbook for that project, but every engagement has its own nuances.

To practice this, copy the template file into your personal repo inside a folder named for the customer and project (for example, "BrightLoop Lead Routing Project"). Then use Claude Code to generate a mock discovery call transcript for that project type. You can prompt it to include edge cases and difficult scenarios specific to that project. Have Claude write the transcript directly into the same folder.

Using AI to Build a Customized Task List​

Once you have the mock transcript, open a second agent session. Prompt it with the playbook, the Teamwork template, and the transcript, and ask it to duplicate the template and create a customized version of the task list based on what was discussed in the call. The result is a tailored task list you could then push into Teamwork.

You can also break it down step by step — ask Claude to create just the milestones with descriptions first, then add the task lists, and then the individual tasks. This lets you see how prompting can push different pieces of the project structure into Teamwork incrementally. If you want to go above and beyond, push some of those tasks into your test project in Academy via the Teamwork API to see the full end-to-end workflow.

Building Your Workflow​

The BR tags you see in the template files are the formatting Teamwork requires for line breaks — that is why the files are formatted that way. Do not worry about cleaning them up.

Stay organized in your personal repo by creating individual folders per customer and per project. As of this recording, the long-term plan is to standardize everything inside the Customer Warehouse so all assets for a given customer live in one place. For now, your personal repo is perfectly fine for Academy practice.

The real goal is to flex your muscles and sharpen the workflow of using AI to customize project management assets. Try projects outside your comfort zone — look at the playbook, review the task list, create mock assets, and practice pushing things into Teamwork. Regardless of where the final standardization lands, the underlying skillset of working with AI in this way is what you will use every single day.