Make the Best Use of Claude โ from foundations to enterprise-scale deployment

Six core pillars to take you from beginner to expert Claude Code practitioner
Master the art of crafting precise prompts to get the most out of Claude for any task.
Understand the complete AI development workflow: plan, execute, review, and refine.
Hands-on project building sessions that take you from idea to a deployed application.
Build autonomous agents, configure subagents, and automate complex multi-step workflows.
Connect Claude Code to external tools via MCP, APIs, hooks, and browser automation.
Enterprise governance, data residency, permissions, sandboxing, and secure AI workflows.
A proven workflow that transforms any idea into a production-ready solution
A structured progression from foundations to advanced enterprise deployment
Foundations & Setup
Definition, control stack, work execution, and suitable tasks
Requirements, installation, verification, Windows native & WSL
IDE setup, workspace creation, monorepos, large codebases
Controls, effort vocabulary, model decision rules, optimization
Inspectable briefs, observing work, guiding sessions, voice input
Safe Working Method & Context
Planning changes, reviewing scope, approval, completion reports
Permission modes, rule evaluation, sandboxing, and recovery
Git workflow, remote repos, AI agent Git use, automated review
CLAUDE.md file, initialization, editing, modular rules, updates
MCP connection, scopes, transports, installation, org control
Advanced Features & Governance
Chrome connection, browser automation, prompt injection safety
Advanced workflows, entry points, limits, research pipelines
Cloud routines, parallel work, file collision prevention, agent teams
Enterprise policies, data governance, observability, cost control
Full pipeline: control, knowledge, automation, verification, human gate
Click any part to see the full module list and learning outcome
Skills you'll take away to confidently work with Claude Code at any scale
Set up and operate Claude Code from zero to production
Apply safe working methods: checkpoints, sandboxing, Git integration
Engineer context and memory for long-running AI sessions
Connect external tools via MCP, hooks, and browser automation
Build autonomous agents and dynamic multi-step workflows
Deploy and operate in cloud and distributed environments
Implement enterprise governance, data policies, and compliance
Complete a capstone research-brief agent from scratch
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