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Claude Code

4 min read

Claude Code is Anthropic’s official command-line interface for Claude, providing direct AI assistance from your terminal for software development, code analysis, and programming tasks.

LLM Model

We are using the GLM model (by Z.AI) instead of Anthropic’s default for Claude Code. The API is served through our own gateway at https://claude.matsci.dev/api.

Installation

System Requirements

  • OS: macOS 10.15+, Ubuntu 20.04+/Debian 10+, or Windows 10+ (with WSL/Git for Windows)
  • Hardware: 4GB+ RAM
  • Software: Node.js 18+ (for NPM installation)
  • Network: Internet connection required

Standard Installation

macOS Installation

Homebrew (Recommended):

brew install --cask claude-code

curl Script:

curl -fsSL https://claude.ai/install.sh | bash

NPM Install (All Platforms)

npm install -g @anthropic-ai/claude-code

Authentication Setup

Info

For DENG Group members, the API credentials will be provided by our computer officer. Please contact the computer officer to get your API access credentials.

Environment Variables

For General Use: Add the following to your ~/.bashrc or ~/.zshrc:

# Claude Code Configuration
export ANTHROPIC_BASE_URL="https://claude.matsci.dev/api"
export ANTHROPIC_AUTH_TOKEN="API-KEY"

Warning

Replace API-KEY with the actual API key provided by the computer officer.

SSH Reverse Proxy Tunnel for HPC Servers

Some machines (e.g., Fornax, Orion) cannot directly reach https://claude.matsci.dev due to network restrictions (firewall, no external access, etc.). In that case, you need to set up an SSH reverse proxy tunnel through a machine that has access.

Setup

From the HPC server, create a tunnel to a bastion machine that can reach claude.matsci.dev:

# Run this on the HPC server (e.g., Fornax, Orion)
# This forwards local port 13000 → bastion → claude.matsci.dev:443
ssh -R 13000:claude.matsci.dev:443 <user>@<bastion-host> -N -f

Then set the environment variable to use localhost:

# Claude Code Configuration for HPC Servers (via tunnel)
export ANTHROPIC_BASE_URL="http://localhost:13000/api"
export ANTHROPIC_AUTH_TOKEN="API-KEY"

Making the Tunnel Persistent

To keep the tunnel alive across disconnects, add to your ~/.bashrc or use autossh:

# Using autossh (install it first if needed)
autossh -M 0 -f -N -o "ServerAliveInterval 30" -o "ServerAliveCountMax 3" \
  -R 13000:claude.matsci.dev:443 <user>@<bastion-host>

Tip

Contact the computer officer for the bastion host details and your SSH key setup.

Apply Changes

After adding the environment variables, restart your terminal or run:

# For bash
source ~/.bashrc
 
# For zsh
source ~/.zshrc

Verification

Verify your installation:

claude --version

Updates

  • Auto updates are enabled by default
  • Manual update: claude update

Basic Usage

# Start in your project directory
cd /path/to/your/project
claude

Best Practices

Project Setup

  • Create CLAUDE.md files for project context and instructions
  • Be specific in your requests and provide relevant context
  • Use /clear to manage conversation context when needed

Effective Workflows

  • Explore, plan, code, commit: Research first, then plan before implementing
  • Use screenshots for visual feedback and UI development
  • Iterative development: Start simple and refine gradually

Advanced Features

  • Git integration: Automatic commit messages and PR assistance
  • Multi-file operations: Work across your entire codebase
  • Custom commands: Create slash commands in .claude/commands/

Security

  • API credentials are provided by the computer officer for group members
  • Store environment variables securely and never share them
  • Do not commit credentials or .bashrc/.zshrc files to version control
  • Always review AI-generated code before committing

References

Next Steps

Ready for Advanced Features?

Once you’re comfortable with basic Claude Code usage, extend its capabilities with MCP (Model Context Protocol) tools:

  • Claude Code with MCP Tools - Add email, calendar, and document processing capabilities
  • Complete research automation workflows can be documented here later if the group develops a stable process.

Common Research Applications

  • Code debugging and optimization for Python scripts
  • Job script generation for HPC clusters
  • Container setup for Docker reproducible research
  • Automated testing and code review workflows
  • Git and Github - Version control integration
  • Conda - Python environment management
  • Docker - Container-based development
  • HPC - HPC job optimization

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