Claude Code Installation Guide for Beginners: From Environment Setup to AI-Built Games

For many everyday users curious about AI-powered programming, Claude Code — Anthropic's command-line AI coding assistant — can seem intimidating because it "runs in a terminal with no graphical interface." But in reality, if you follow the setup steps methodically, even someone with zero programming experience can get everything running within an hour and have AI write a fully playable game for you.

This article is based on a hands-on tutorial by the Bilibili creator "Star Sky" (星空), organized into a complete walkthrough from zero-install to delivering a finished project, with additional notes on key pitfalls to watch out for.

Understanding Claude Code: Your AI Programming Partner in the Terminal

Claude Code is an AI coding assistant that runs in the command line. It doesn't have a fancy graphical interface, but it can directly write code, modify projects, debug issues, and even handle deployments. As the original tutorial puts it, "It runs in the command line — no flashy interface — yet there's a certain romance to it that belongs to programmers."

What sets Claude Code apart from graphical AI coding tools like ChatGPT or Cursor is that it runs directly in your operating system's Terminal/Shell environment. The command-line interface (CLI) is the most primitive yet most powerful way to interact with a computer — all graphical interfaces are essentially wrappers around CLI operations. By choosing the CLI route, Claude Code gains direct control over file systems, process management, network requests, and other low-level operations. It can work like a real programmer sitting at your computer — creating files, editing code, running scripts, and debugging errors — rather than simply outputting code snippets inside an isolated chat window. This "agentic coding" paradigm is becoming the dominant trend in AI-assisted development in 2025.

Unlike chatting with an AI in a browser, Claude Code can directly read and write your local files, execute commands, and manage your entire project structure. This means it doesn't just "give you a piece of code" — it can roll up its sleeves and get to work inside your project directory, just like a real developer. For beginners, this "fully delegated" experience actually lowers the barrier to getting things done.

Environment Setup: Install the Four Essential Components

To get Claude Code up and running, you first need to set up the foundational environment. While there are quite a few steps, each one is a standard "click Next" installation. The four core components are:

1. Node.js (Core Runtime)

Claude Code runs on Node.js, making this the essential first install. Node.js is a JavaScript runtime built on Chrome's V8 engine, created by Ryan Dahl in 2009. Its core value lies in enabling JavaScript — a language originally confined to browsers — to run on servers and in local command lines. Claude Code itself is an npm package written in JavaScript/TypeScript, so it depends on Node.js to execute. Installing Node.js automatically includes npm (Node Package Manager), the world's largest open-source package manager with over 2 million packages. It's recommended to install the LTS (Long-Term Support) version rather than the latest release to ensure stability.

During installation, watch for UAC permission prompts. After installation, be sure to run verification commands in the terminal — a returned version number confirms success:

node -v
npm -v

2. Python and pip

When installing Python, there's one critical detail: check both boxes related to "Add Python to PATH," then select custom installation. Many beginners miss this checkbox, which causes the command line to fail to recognize Python later.

PATH is an environment variable in the operating system that tells the system which directories to search for executable programs. When you type python in the terminal, the system searches through the paths listed in the PATH variable one by one until it finds the Python executable. If you don't check "Add Python to PATH" during installation, Python will be installed on your hard drive, but the system won't know where to find it and will return errors like "python is not recognized as an internal or external command." pip is Python's package management tool (similar to npm for Node.js), used to install and manage third-party Python libraries. Claude Code may invoke Python scripts or depend on tools from the Python ecosystem in certain scenarios, making Python a recommended part of the setup.

Verify in the terminal after installation:

python --version
pip --version

3. Git (Version Control)

Git is a distributed version control system developed by Linux creator Linus Torvalds in 2005, and it has become the de facto standard for software development worldwide. Its core concept is creating a "snapshot" (commit) for every modification to a project. These snapshots form a timeline, allowing developers to revert to any historical version at any time. Git doesn't store complete file copies but rather the differences (diffs) between files, making it highly efficient.

Git is used for subsequent code management and deployment. During installation, make sure the path contains no Chinese characters, spaces, or special characters other than English — this is because Git's underlying file operations rely on ASCII path parsing, and non-ASCII characters may cause path recognition failures or garbled text. This is an easily overlooked pitfall that frequently triggers errors. Verify the version number after installation.

4. Claude Code Itself

Once the environment is ready, install Claude Code via the command line and verify the installation was successful. At this point, the basic environment setup is complete.

Tip: Throughout the entire process, never skip any step that asks you to "verify the version number." A returned version number is the only reliable signal that a component was installed correctly. If any step fails, subsequent steps will almost certainly trigger cascading errors.

Configuring the Model Channel with CC Switch

Having the environment installed doesn't mean you can start using it right away — without configuring a model channel, Claude Code will throw numerous errors and be unusable. This is where the tutorial introduces a key tool called CC Switch, used to configure and switch between model channels.

CC Switch essentially acts as a proxy layer between Claude Code and different LLM APIs. An API Key (Application Programming Interface Key) is an authentication mechanism — when you send requests to model providers like DeepSeek, the API Key proves that "this request comes from a legitimate paying user." LLM business models typically charge by Token: a Token is the smallest unit of text processing, where one Chinese character equals roughly 1–2 Tokens, and one English word equals roughly 1–1.5 Tokens.

The configuration steps are roughly as follows:

• Open CC Switch and add a new channel;
• The tutorial selected DeepSeek as the model source (for cost-effectiveness);
• Go to the DeepSeek website to apply for and create an API Key, add a note, and copy the key;
• Paste the API Key into CC Switch and fetch the model list;
• Select the cost-effective Flash model;
• Click "Test" — a green indicator means the channel is connected successfully.

The tutorial chose the DeepSeek Flash model because it strikes a good balance between inference speed and price — compared to Anthropic's official Claude API, third-party model costs can be an order of magnitude lower, making them especially suitable for learning and experimentation. It's particularly important to note that an API Key is like your "digital key" — never share it publicly or upload it to a public code repository.

This step is where beginners most commonly get stuck. After the connectivity test passes, type claude in the terminal to enter the Claude Code interface, where you can select Dark Mode on first launch.

Hands-On Demo: Let AI Build a Minesweeper Game

Once the environment is up and running, the tutorial demonstrates Claude Code's complete workflow with a classic example — a Minesweeper game.

Installing an Essential Skill

The tutorial specifically recommends installing a Skill (plugin), calling it "the only one I recommend — essentially a must-install." A Skill is an extension mechanism in the Claude Code ecosystem, similar to browser extensions or IDE plugins. It uses predefined prompt templates and workflows to make Claude Code perform more professionally in specific scenarios.

Installation is extremely simple: just send the installation command to Claude Code, and it will handle the rest automatically. During installation, select Auto Mode (fully automatic mode) by toggling with the Shift + Tab shortcut. Auto Mode is a concrete embodiment of the AI Agent concept — in this mode, the AI is no longer a passive chatbot answering questions but a "digital employee" with autonomous decision-making capabilities, able to plan steps, execute operations, and verify results independently, so you don't have to manually type "Yes" to confirm every action.

When configuring the Skill, it's recommended to enable Web Output, which displays Claude Code's thinking process. This feature leverages the Chain of Thought display mechanism of large language models, allowing users to observe the AI's reasoning process — crucial for building trust and catching potential issues, and helpful for beginners to understand "what it's actually thinking." Skills are invoked with the format /skill + what you want to do.

Start with Planner Mode to Clarify Requirements

This is the most valuable lesson in the entire workflow: don't jump straight into having AI write code. The tutorial first switches to Planner Mode — in this mode, the AI only reviews and plans without actually adding, deleting, or modifying any code.

Planner Mode embodies a classic software engineering principle: requirements analysis before implementation. In traditional software development, unclear requirements are the number one cause of project failure — studies show that fixing a requirements-stage error costs only 1/10 to 1/100 of catching the same error during the coding phase. Claude Code's Planner Mode applies this principle through AI: it uses structured Q&A to guide users in clarifying feature scope, technology choices, UI preferences, and other key decisions, essentially functioning as an automated requirements analyst.

Once in Planner Mode, the AI proactively asks a series of questions (Q1, Q2, Q3...), going round by round to understand what features you want. The core principle here is: the more detailed your answers, the better the AI understands your requirements, and the lower the probability of bugs in the final result. The AI also provides recommended options that are usually quite good, but watch out for occasional "hallucinations" — a known limitation of large language models where the model sometimes confidently outputs non-existent APIs, incorrect technical details, or fabricated feature proposals. Even with AI assistance, human judgment and review remain indispensable.

Switch to Full Auto Mode and Let AI Work

After several rounds of Q&A to confirm requirements, switch to full auto mode and the AI begins working independently, no longer asking frequent questions. The demo shows it chose an HTML + CSS + JavaScript tech stack to build this web-based Minesweeper.

HTML, CSS, and JavaScript are known as the "three musketeers" of web front-end development, each with a distinct role: HTML (HyperText Markup Language) handles content structure, defining what elements are on the page; CSS (Cascading Style Sheets) handles visual presentation, determining how those elements look; JavaScript handles interaction logic, controlling how elements respond to user actions. The AI's choice of this tech stack is highly reasonable — it requires no backend server, all logic runs in the browser, and the resulting single HTML file can run independently, making it ideal for deployment via static hosting services.

As long as there are animated indicators at the bottom of the interface, the AI is still thinking and hasn't finished yet. Once it delivers the result, open the generated HTML file to test it. In the tutorial, the Minesweeper game "has complete functionality with basically no bugs" and is a remarkably faithful recreation.

From Local to Live: Deploying with GitHub Pages

Building the game isn't enough — the tutorial goes further to demonstrate how to deploy your work online so others can play it directly in their browsers.

This is where Git's value shines again: Claude Code can automatically handle GitHub commits and deployment for you, eliminating worries about manual operation errors. More importantly, uploading to GitHub after each version is essentially an automatic backup for your project — even if code is lost locally, it can be recovered. Red marks show deletions, green marks show additions, and changes are crystal clear.

GitHub Pages is a free static website hosting service provided by GitHub. It reads HTML, CSS, JavaScript, and other static files directly from a GitHub repository and serves them via a username.github.io domain. Unlike traditional website deployment that requires purchasing a server, configuring a domain, and installing web server software, GitHub Pages simplifies the entire process down to "push code to a GitHub repository." It supports custom domains and HTTPS encryption, is completely free for public repositories, and offers 100GB of monthly bandwidth — more than enough for personal projects and small applications.

Ultimately, after deploying via GitHub Pages, a publicly accessible URL is generated that can be opened directly to play the game online. The entire process — from environment setup, to AI programming, to going live — forms a complete end-to-end workflow.

Final Thoughts

The greatest value of this tutorial isn't "teaching you how to install software" — it's demonstrating a development paradigm for ordinary people in the AI era: first use Planner Mode to clearly articulate requirements, then use full auto mode to let AI execute, and finally leverage Git and GitHub for version management and deployment.

As the original creator put it, "We're in the AI era now — there's basically no problem that can't be solved." For beginners, the most direct approach when encountering any error is to throw the problem at AI. The real barrier may no longer be "whether you can write code" — it's "whether you dare to start."