Documentation Index
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Anthropic Training home page

Claude Enterprise
Claude Desktop

Getting Started

Welcome
How to use
Goals

Foundations

Overview
AI at work
Mental model
Fluency loop
When not to
Lab: Diagnose

Claude Landscape

Overview
Web vs Desktop
Surfaces map
Add-ins
Sharing
Lab: Three ways

Models & Modes

Overview
Choosing models
Thinking
Tool use
Agentic loop
Stack vocabulary
Lab: Compare

Context & Cost

Overview
Context is prompt
Where context lives
Hygiene
Token literacy
Compression
Lab: Project brief

Real Work

Overview
Deep research
Enterprise search
Analysis
Docs & decks
Ops & admin
Comms
Lab A: Research
Lab B: Live surface

Projects & Artifacts

Overview
Project setup
Artifacts
Handoffs
Lab: Durable Project

Skills

Overview
What is a skill
Using skills
Caveman skills
Building skills
Skills vs sub-agents
Lab: Ship a skill

Connectors & Cowork

Overview
Connectors
Cowork
Failure modes
Lab: Least privilege

MCP

Overview
What is MCP
MCP vs APIs
Tools & resources
Using vs building
Choosing options
MCP safety
Lab: Configure MCP
Stretch: Custom MCP

Agents

Overview
Loops in practice
Managed agents
First managed agent
Sub-agents
Code & tools
Lab: Managed agent
Stretch: Code tool

Evaluation

Overview
Judging outputs
Iterate
Different bars
Lab: Rubric

Safety & Ethics

Overview
What to share
Enterprise sharing
Ethics
Lab: Red-team

Capstone

Overview
Analyst pack
Ops pack
Power pack

Resources

Overview
Cheatsheets
Glossary
Further learning

MCP

What MCP is

Think of MCP as a USB-C port for AI tools and data.

MCP deep module overview

How MCP relates to APIs

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