A practical guide to thinking about AI — from your first hands-on exercise to compound production systems.

Mastering Claude: The Org Age of AI

A hands-on guide to building intelligence into how your organization actually works.

8 chapters · 27 exercises · Will Schenk

Why This Course Exists

You don’t need a bigger model. You need a smarter operation.

Most AI advice starts with the technology. This starts with your work. The invoices you process, the reports you check every morning, the rules that live in one person’s head. The stuff a COO worries about — not what model to pick, but how to make the organization run better.

Every chapter gives you something to do right now. Open Claude, ask a real question, see where it breaks. Dictate your rules on your phone while walking through the warehouse. Drop a spreadsheet into Co-work and watch it find errors you missed. Install a tool, connect your data, set up a schedule. By the end, you’ve built a system that categorizes transactions, processes invoices, flags anomalies, and improves every time you correct it.

The running example is Cornwall Market — a grocery store and bakery with fifteen suppliers, manual invoice processing, and categorization rules that live entirely in the owner’s head. If that sounds familiar, you’re in the right place. If your version is a law firm, a construction company, or a SaaS team — the pattern is identical. Undocumented knowledge, manual data entry, tribal expertise that doesn’t scale.

This is not a prompting guide. It’s a build guide. Each chapter introduces a new tool, gives you exercises with expected outcomes, and connects what you build to everything you’ve already built. The pieces compound. That’s the whole point.

The Four Transitions

The path from tribal knowledge to compound systems

Four transitions. Each builds on the one before. The early ones are things anyone can try — describe how you work, connect your tools. The later ones get deeper. Start where you are.

Transition 1: Making the Org Legible. Can the company describe its own work to a machine? Chapters 01–02 · Skills · Audience: Everyone

Transition 2: Trusting Your Own Data. Can people act on what the system tells them? Chapter 03 · MCP · Audience: Teams

Transition 3: The System Starts Acting. Does the system surface insights before being asked? Chapters 04–06 · Dispatch, Channels, Computer Use · Audience: Integrators

Transition 4: The System Changes the Org. Does the system learn from its own operation? Chapters 07–08 · Habitats, Evals · Audience: Builders

The AI Maturity Framework

Most teams are at L0 or L1 — and that’s fine. This course starts there. The first two chapters are about making the jump to L2: describing your work in a way machines can follow. Everything else builds from that foundation.

Level	Name	Your Organization…
L0	Tribal	Runs on tacit knowledge and habit. Processes live in people’s heads.
L1	Experimenting	Uses AI individually. Nothing shared, nothing compounding.
L2	Legible	Can describe its own work to a machine. Tribal knowledge becomes shareable instructions.
L3	Knowledgeable	Knows what it knows. Proprietary data is connected with verification infrastructure.
L4	Adaptive	System surfaces insights before being asked. Delegation tools are in place.
L5	Self-Improving	System learns from every interaction. Eval and feedback loops close the circle.

How to Read This

Start accessible, go as deep as you want

Depth 1 — Foundations. Chapters 01–02. No code required. Write your first skill. Understand where your org sits. Exercises anyone can try today.

Depth 2 — Integration. Chapters 03–04. Connect real data. Start delegating. Leaders and builders both benefit — from different angles.

Depth 3 — Delegation. Chapters 05–06. The system acts on what it sees. Channels, schedules, Computer Use. Real automation.

Depth 4 — Production. Chapters 07–08. Habitats, evals, compound systems. For teams building production agent infrastructure.

Chapter Listing

Chapter	Title	Description	Level	Tools
01	The Org Age of AI	The map, the framework, and why the middle layers matter	L0 → L1	Claude.ai
02	Making Your Organization Legible	Write a skill — no code required	L1 → L2	Claude mobile app
03	Trusting Your Own Data	Connect your real data. MCP makes every tool available everywhere	L2 → L3	Co-work, MCP
04	Delegation, Not Prompting	The system starts acting on what it sees	L3 → L4	Claude Code, Remote Control
05	The Always-On System	Channels, schedules, and the system that works while you sleep	L4	MCP, Scheduled Tasks
06	When APIs Don’t Exist	Computer Use closes the last gap	L4	Chrome DevTools, Computer Use
07	Running Agents in Production	Habitats, evals, and production agent infrastructure	L4 → L5	Umwelten, Habitats
08	The Compound System	Everything multiplies — capabilities times access methods	L5	Full stack

The Concrete Build Map

Chapter	Transition	What You Build	Cornwall Market Arc
01	Orientation	Your legibility gap list	Sarah discovers what Claude doesn’t know
02	T1: Legibility	Your first SKILL.md	Sarah dictates vendor rules, creates a categorization skill
03	T2: Trust	MCP connections to your data	Cornwall Market’s QuickBooks and invoice data become accessible
04	T3: Delegation	Dispatch and agent workflows	Invoice processing moves from manual to agent-assisted
05	T3: Always-on	Scheduled tasks and channels	Daily invoice processing runs automatically
06	T3: Last mile	Computer Use for systems without APIs	Handling the vendor portal that only has a web interface
07	T4: Production	Agents with eval and feedback	The system learns from Sarah’s corrections
08	T4: Compound	The full system	All pieces working together, continuously improving

The Distillation Pipeline

There is a second thread running through the course: the distillation pipeline. Every conversation you have with Claude is a potential source of organizational knowledge. The course follows a progression from the most informal capture to the most formal:

Form	What It Is	Chapter
Conversation	You talk to Claude, explain how things work	Ch 01
CLAUDE.md	Notes from conversations saved as persistent instructions	Ch 02
SKILL.md	Structured knowledge with rules, tables, exceptions	Ch 02
MCP server	Knowledge exposed as tools any surface can call	Ch 05
Habitat	Self-modifying agent that updates its own skills from corrections	Ch 07

The key insight: don’t just ask Claude to do things. Ask it to document how it did them so it can do them again. Every discovery gets documented back into the system. That is how a conversation becomes an agent.

The Claude Ecosystem

This course uses Claude’s ecosystem as the worked example. Three surfaces:

Surface	What It Is	Best For	Used In
Claude.ai	Web and mobile chat interface with artifacts and analysis	Conversation, quick tasks, phone-based capture	Ch 01–02
Co-work	Desktop app with sandboxed code execution and browser access	Knowledge work, research, complex documents	Ch 03–06
Claude Code	Terminal-native agent with full filesystem, shell, and tool access	Development, deployment, production automation	Ch 07–08

Connecting all three surfaces is MCP — the Model Context Protocol, a standard that lets any tool, service, or data source expose itself to Claude through a single interface.

Who This Is For

Anyone trying to figure out what AI means for how they work. You don’t need to be a developer. You don’t need to be a CEO. The first two chapters are for everyone — hands-on exercises, no code required.

As the course progresses, the material gets more specialized: connecting data, building delegation, running production agents. Start wherever you are. If you’ve never written a skill, start at Chapter 01. If you’re already integrating tools, jump to Chapter 03. If you’re building compound systems, go to Chapter 07.

Based on real deployments at bookkeeping firms, construction companies, media analytics, financial services, and Tezlab. These aren’t hypothetical patterns.

hey@thefocus.ai · thefocus.ai