AI for teams: scale your personal AI operating system to your group
Turn your solo AI system into a shared team setup your whole group can run without chaos. For students and early-career professionals who've built a personal AI operating system and need it to work across a team without losing consistency or ownership.
3 chapters, 7 lessons
14-day refund on the yearly plan. Real pricing on the plans page, no surprises.
7
lessons, yours to run
Easy to pick up, built to get you the next job
No coding background needed, and you write code with Claude as you go.
Every section below turns into something a hiring manager recognises, not just notes.
Diagnose exactly what breaks when a second person joins your system
- You will identify the three failure points that hit every growing team at once: conflicting context, inconsistent conventions, and unclear ownership
- You will map those failure points to the same structural problems real organisations pay consultants to fix, at a scale you can actually solve in one course
- No coding background needed: this starts with a case study and a workbook, not a command line
Build a shared repository that signals genuine team collaboration to anyone reviewing your work
- You will create one GitHub repo with multiple active contributors, a shared CLAUDE.md written with the whole team, a skills folder, and written contribution rules
- You will replace four separate personal setups with one agreed system, so there is no reconciling at the last minute
- A shared repo with real contributors is a portfolio artefact a hiring manager reads differently to a solo project
Write and test a 30-minute onboarding document that actually works
- You will draft an ONBOARDING.md that takes a brand new teammate from opening the repo to making a real first contribution in one sitting
- You will run a live test on an actual teammate, log every moment the document fails them, and revise based on what you observed, not what you guessed
- Fast onboarding is one of the capabilities employers pay for on collaborative teams, and this gives you a tested document to prove it
Configure four role-based sub agents that match how your team actually divides work
- You will set up researcher, drafter, reviewer, and formatter sub agents with specific written job descriptions tied to your shared CLAUDE.md
- You will run a low-stakes test task through all four roles before trusting the system on a real deliverable
- Configuring and orchestrating sub agents mapped to a real workflow is the kind of technical capability that separates operators from people who only know the theory
Deliver one complete group project on the shared harness, for real
- You will execute an actual graded assignment through the shared repo, shared conventions, and configured sub agents, with every teammate owning a genuine role
- You will catch at least one real inconsistency through the reviewer role before submission, which is the proof the system is working, not only installed
- Every skill across this course runs live on one real deliverable, giving you a concrete story to tell in any interview or job application
Finish this course and you can do all of this, no prior background required:
- You will be able to set up a shared team repository with agreed conventions and written contribution rules, a collaboration skill employers and project leads actively look for.
- You will be able to write and test an onboarding document that gets a new contributor productive in under 30 minutes, demonstrating the fast-onboarding capability that makes you valuable on any collab
- You will be able to configure role-based sub agents with specific written job descriptions, so divided work stays consistent instead of being reconciled at the last minute.
- You will be able to orchestrate a researcher, drafter, reviewer, and formatter pipeline through a shared system, the kind of hands-on orchestration experience that separates operators from people who
- You will be able to run a complete group project end to end on a shared AI harness and submit a real deliverable where every role, convention, and standard held together, giving you a concrete story f
- You will be able to catch inconsistencies through a structured reviewer role before submission, demonstrating quality control discipline that hiring managers recognise in candidates who have worked in
7 lessons, 3 chapters
Built from real implementation work
Every agent, skill file, and dataset is drawn from 25+ years and 50+ Oracle ERP implementations across pharma, manufacturing, semiconductor, and distribution.
Kept current with the stack
Claude Code, skills, and MCP move fast. Your licence includes every system update for as long as you're subscribed, so what you build keeps working as the tools change.
AI for teams starts where your solo system stops working. Your personal AI operating system works well for you alone. The moment a second teammate touches it, things fall apart. Your CLAUDE.md reflects your habits, files get named four different ways, and nobody agrees whose version is the right one. Group projects end in a two-day scramble to reconcile pieces that were never consistent. The guides you find assume one person working alone. This course shows you how to make one system work across a whole team.
What you'll be able to do, module by module
Every module leaves you able to build and run something real. Here is the syllabus, in the order you work through it.
Curriculum
3 chapters · 7 lessons
Section 1: From Personal to Shared
You can diagnose the three structural problems that break every team system the moment a second person joins, and explain the specific decisions that fix each one, using a concrete case study as your evidence.
Chapter 1.1: What Changes at 4 People3 items
- L1: Shared Context, Conventions, and Ownership🔒
- L2: Case Study: A 4 Person Class Project That Almost Collapsed🔒
- Quiz: Scaling BasicsQuiz
Section 2: The Shared Repo
You can create and configure a shared GitHub repository with multiple active contributors, a team-written CLAUDE.md, a skills folder, written contribution rules, and a tested onboarding document that gets a new teammate to their first real contribution in one sitting.
Chapter 2.1: Setup and Onboarding3 items
- L3: Workbook: Create the Team Repo🔒
- L4: Workbook: The 30 Minute Onboarding Doc🔒
- Quiz: Team Repo RulesQuiz
Section 3: Sub Agents and Delivery
You can configure four role-based sub agents mapped to your team's actual workflow, test the handoffs between roles before they matter, and run a complete real group assignment through the shared harness so every teammate owns a genuine role and the final deliverable is consistent end to end.
Chapter 3.1: Divide the Work, Keep the Standard4 items
- L5: Workbook: Configure 4 Role Based Sub Agents🔒
- L6: Workbook: Deliver One Group Project on the Harness🔒
- L7: Workbook: The Retrospective🔒
- Course Final: Team HarnessQuiz
You'll leave with a shared repository with several contributors, a tested onboarding document, and a real group deliverable run through a configured sub agent pipeline. That shows a hiring manager you can work inside a team system as well as build one for yourself.
From subscribing to running your first artifact
Subscribe and open the course workbook
Log in, open Section 1, and read the case study of a four-person project that almost collapsed and the three decisions that pulled it back. No setup required before lesson one.
Build the shared repo and onboarding doc with your team
Follow the workbook steps to create a GitHub repository your whole team contributes to, write a shared CLAUDE.md together, set up a skills folder, and produce a CONTRIBUTING.md. Then write and live-test your ONBOARDING.md until a real teammate reaches their first contribution without asking you for help.
Configure your sub agents and deliver the real project
Set up the four role-based sub agents, researcher, drafter, reviewer, formatter, mapped to how your team actually splits work. Run a test task through all four roles, then execute your real group assignment on the shared harness and submit it. That completed project is your portfolio proof.
How a four-person team delivers a research report without a last-minute scramble
Agree the shared context before anyone starts
The whole team sits down and writes one CLAUDE.md together, covering the report's scope, citation style, and formatting standard. Nobody starts gathering sources until this file exists and everyone has read it.
Assign each teammate a sub agent role that matches how they actually work
The teammate who always finds the sources takes the researcher role. The one who writes first drafts fastest takes the drafter. The detail-oriented one takes the reviewer. The one who handles layout takes the formatter. Roles are written down in the shared repo, not assumed.
Run one small test section through all four roles first
Before touching the real report, the team runs a single short section through the researcher, drafter, reviewer, formatter chain. They check that handoffs between roles are clean and that the output matches the shared CLAUDE.md standard. They fix the one place the drafter's output did not follow the agreed citation format before it becomes a pattern across eight sections.
Execute the full report in parallel, not in sequence
With roles clear and handoffs tested, each section moves through the pipeline simultaneously rather than waiting for one person to finish before the next starts. The reviewer catches two inconsistencies in heading style before the formatter ever sees the file.
Submit one consistent deliverable with a clear audit trail in the shared repo
The final report is submitted from the shared repo. Every change has a named contributor. The reviewer's caught inconsistencies are logged. The team has a real record of how the work was done, not just the output.
The toolkit you'll build and run in this program
You leave with working assets, not notes.
Real things you can demo in an interview.
A shared GitHub repository with multiple active contributors, a team CLAUDE.md, a skills folder, and a CONTRIBUTING.md: a portfolio artefact that signals real team collaboration
A tested ONBOARDING.md with a real friction log and revision history, proving you can onboard contributors fast, not just claim you can
Four configured role-based sub agents, researcher, drafter, reviewer, formatter, with specific written job descriptions tied to your team's shared standards
A completed real group project submitted through the shared harness, with documented evidence of roles held and conflicts caught before submission
You'll get the most from this if
Students running group projects who are tired of reconciling four inconsistent pieces the night before a deadline
Early-career professionals who have a personal AI system from a previous course and need to extend it to a small team
Anyone who wants to show a future employer they can collaborate with AI inside a team
Project leads who need to onboard teammates to a shared AI workflow quickly and consistently
Yearly plan: 14-day keep-the-system guarantee
14-day refund on the yearly plan. Finish the first two modules, and if you haven't run your first live pipeline, take a full refund.
Straight answers before you subscribe
How is this course on AI for teams different from building a personal AI system?
AC 202 builds a system for one person. AC 203 converts it into a shared team setup: a shared repo, written conventions, an ONBOARDING.md and a CONTRIBUTING.md, and sub agents configured for divided work. You test the onboarding document on a real teammate, with the aim of bringing them up to speed in 30 minutes. Then you deliver one full group project on the system.
Do I need a coding background to follow this course?
No. The workbook steps are written in plain language for people who have not coded before. You will use a few basic terminal commands like git clone and mkdir, which are explained the first time they appear. If you got through AC 202, you have everything you need to start lesson one.
Does this work if my team is not in the same room or on the same schedule?
Yes. The shared repo and written conventions are designed for teams that don't work side by side. The ONBOARDING.md and CONTRIBUTING.md let a teammate come up to speed asynchronously, and the sub agent roles mean work can happen in parallel without constant coordination.
What if I do not have a team right now? Can I still take this course?
The workbook lessons work best with real teammates, since the onboarding document test needs a real person to follow it. You can start the concepts and the repo setup alone. The daily live sessions give you access to a mentor and other students you can practise with if you need a stand-in team.
What is the refund policy?
Refunds depend on the plan. The yearly plan has a 14-day full refund. In India every plan is a one-time pass that isn't refunded, but it never renews without you, and you can try a free lesson before you buy. The refunds page has the full policy.
How are updates handled, and can I use what I build commercially?
The course is updated as the tools and practices it covers develop, and enrolled students get those updates at no extra cost. Everything you build belongs to you and your team: your shared repo, your sub agent configurations and your onboarding document. You can use it on real work projects.