Claude Code Ultimate Guide

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Learn Claude Code, build reliable agents, and scale their use safely. The website is the primary reading and discovery interface. This repository contains the canonical Markdown sources, reusable files, machine-readable indexes, and contribution history.

Start here: complete a first task online · browse the guide portal · open the complete sitemap · read the Markdown source

Choose your next step

The table below is generated from machine-readable/navigation.json. The same contract feeds the public sitemap, so the repository and website expose the same intent model.

<!-- BEGIN GENERATED INTENT NAVIGATION -->

| Intent | Browse online | |---|---| | Start | Guide portal · Quick Start · Learning Paths · Quick Reference · AI Roles | | Build | Agent Harness Engineering · Loop & Graph Engineering · Context Engineering · Memory Systems · Workflows · Methodologies · MCP or CLI? · Examples | | Scale | Security · Enterprise Governance · Observability · Team Metrics · Team Adoption · Subscription Strategy · AI Unit Economics · Team Knowledge · API Gateway | | Resources | Resource Hub · Downloads · Cheat Sheets · Ebooks · Diagrams · Ecosystem · Compare · Glossary · FAQ · Guide MCP Server · Related Projects | | Updates | Guide Changelog · Claude Code Releases · RSS Feed |

<!-- END GENERATED INTENT NAVIGATION -->

Why this guide exists

Claude Code documentation explains the product. This guide connects product behavior to engineering decisions: what belongs in context, when to use an agent instead of a skill, how to verify generated work, and which controls matter when usage moves beyond one developer.

| Need | Start with | |---|---| | Understand Claude Code internals | Architecture and Tools Reference | | Design agent systems | Agent Harness Engineering and Loop & Graph Engineering | | Structure AI-assisted delivery | Methodologies and Workflow Guides | | Establish a security boundary | Security Hardening and Sandbox Isolation | | Validate understanding | Knowledge Quiz and Recap Cards |

The guide favors explicit trade-offs and verifiable procedures. Where evidence is incomplete, the relevant page should preserve that limit instead of presenting one workflow as universal.

Start

Install Claude Code

Choose one installation method:

# npm, macOS, Linux, or Windows
npm install -g @anthropic-ai/claude-code

# macOS with Homebrew
brew install claude-code

# macOS or Linux native installer
curl -fsSL https://claude.ai/install.sh | sh

Windows PowerShell also supports:

irm https://claude.ai/install.ps1 | iex

Verify the installation and authenticate:

claude --version
claude doctor
claude auth login

The Quick Start chapter documents installation alternatives, authentication, updates, permission modes, and common first-day failures.

Complete a first task

Open a small repository with a clean or understood Git state, then start Claude Code:

cd your-project
claude

Give Claude a bounded request that includes the expected result and verification:

Explain how this project runs its tests. Do not modify files.
Name the relevant commands and cite the files that define them.

Before asking Claude to edit code, add a project-level CLAUDE.md that records the commands and constraints Claude must follow:

# Project instructions

## Commands
- Test: `npm test`
- Lint: `npm run lint`

## Boundaries
- Do not edit generated files.
- Do not change dependencies without approval.
- Run the relevant tests before claiming completion.

Continue with the first workflow or use the starter CLAUDE.md templates.

Choose a learning path

| Situation | Suggested route | |---|---| | New to Claude Code | Seven-module learning path | | Already using the CLI | Core Concepts, then Context Engineering | | Senior developer or tech lead | Methodologies, Agent Harness Engineering, then Production Safety | | Engineering manager or CTO | Adoption Approaches, Team Metrics, then Subscription Strategy | | Security or platform role | Security Hardening, Enterprise Governance, then Observability | | Product manager or designer | Product Manager Guide or Design-to-Code Workflow | | Non-developer knowledge worker | Claude Cowork Guide |

For a personalized route, use the repository onboarding prompt:

claude "Fetch and follow the onboarding instructions from: https://raw.githubusercontent.com/FlorianBruniaux/claude-code-ultimate-guide/main/tools/onboarding-prompt.md"

Build

Agent engineering

An agent is one component of a larger system. The surrounding harness controls context, tools, permissions, state, stopping conditions, recovery, and evaluation.

| Resource | Decision it supports | |---|---| | Agent Harness Engineering | Identify the controls required around an agent loop | | Loop & Graph Engineering | Choose bounded feedback loops or explicit workflow graphs | | Agent Harness Map | Distinguish runtimes, orchestrators, frameworks, control planes, and support tools | | Agentic Tools | Compare selected coding agents and orchestration products | | Agent Evaluation | Test behavior, regressions, and task-level outcomes | | Harness Glossary | Align terminology across design and review |

Context and memory

Context quality affects every tool call and decision. Start with project instructions, add specialized context only when a recurring task needs it, and test whether the extra material changes behavior.

| Resource | Focus | |---|---| | Context Engineering | Context budget, modular instructions, assembly, and measurement | | Memory Systems | Native memory, cross-session systems, team sharing, and retention risks | | Context Engineering Tools | Output compression, retrieval, gateways, and context inspection | | Context Audit | Measure a project's context architecture | | Team AI Instructions | Maintain shared instructions across a development team |

Workflows

| Goal | Workflow | |---|---| | Implement with tests first | TDD with Claude | | Define behavior before implementation | Spec-First Development | | Separate planning from execution | Plan-Driven Development | | Compare independent candidates | Best-of-N | | Coordinate several agents | Agent Teams | | Build bounded autonomous loops | Agentic Software Factories | | Review code systematically | Code Review | | Prepare a contribution another team can review | AI-Assisted Open Source Contributions | | Diagnose unfamiliar repositories | [Exploration Workflow](./g