Building Cross-Platform CLI Tools with AI

Command-line tools remain essential to developer workflows. Whether it's a deployment script, a data processing utility, or a development helper, CLIs provide fast, scriptable interfaces that integrate naturally with existing toolchains. But building tools that work correctly across operating systems has traditionally been tedious work. AI changes this equation dramatically.

The Cross-Platform Challenge

Every operating system has its quirks. File paths use different separators. Signals work differently. Environment variables have different conventions. Process management varies significantly. A tool that works perfectly on your Mac might fail mysteriously on a colleague's Windows machine or a Linux server.

Historically, addressing these differences meant extensive research, platform-specific code branches, and access to multiple operating systems for testing. AI assistants like Claude Code can dramatically accelerate this process because they understand these platform differences implicitly.

Why AI Excels at CLI Development

AI is particularly well-suited for CLI tool development for several reasons:

• Pattern recognition: AI understands common CLI patterns - flag parsing, subcommands, configuration files - and can implement them idiomatically
• Platform knowledge: AI knows the differences between Unix and Windows signals, path handling, and system calls
• Library awareness: AI can recommend and correctly use platform-appropriate libraries and abstractions
• Error handling: AI anticipates platform-specific failure modes and implements appropriate handling

Practical Example: A Build Automation Tool

Let's say you need a tool that watches a directory and triggers builds when files change. With Claude Code, you might start with:

> Build a file watcher CLI in Go that triggers a
  command when files in a directory change. It should:
  - Accept the directory and command as arguments
  - Support a debounce interval flag
  - Work on macOS, Linux, and Windows
  - Handle graceful shutdown on Ctrl+C

Claude Code would generate a complete implementation, handling platform-specific considerations automatically. On Unix systems, it would use standard signal handling. On Windows, it would implement the appropriate console control handlers. File watching would use the correct APIs for each platform.

Key Cross-Platform Considerations

File Paths

Never hardcode path separators. Use your language's path manipulation library (filepath in Go, pathlib in Python, path in Node.js). AI will typically generate correct code here, but always verify path handling in cross-platform contexts.

Process and Signal Handling

Unix signals don't exist on Windows. If your tool needs to handle Ctrl+C gracefully, you'll need platform-specific code. AI understands this and can generate appropriate abstractions or conditional implementations.

Terminal Interaction

Color support, terminal width detection, and interactive prompts all work differently across platforms and terminal emulators. Libraries like Cobra (Go), Click (Python), or Chalk (Node.js) abstract many of these differences.

Configuration Files

Where should your tool store configuration? On macOS, it's ~/Library/Application Support. On Linux, it's ~/.config or $XDG_CONFIG_HOME. On Windows, it's %APPDATA%. AI knows these conventions and can implement appropriate config file locations.

Building and Distributing

Once your tool works, you need to get it to users. AI can help set up:

• Cross-compilation: Build binaries for all target platforms from a single machine
• CI/CD pipelines: Automated builds and releases via GitHub Actions or similar
• Installation scripts: Shell scripts for Unix, PowerShell for Windows
• Package managers: Homebrew formulas, AUR packages, Chocolatey packages

# Example: Cross-compile Go for all platforms
GOOS=darwin GOARCH=amd64 go build -o dist/mytool-darwin-amd64
GOOS=darwin GOARCH=arm64 go build -o dist/mytool-darwin-arm64
GOOS=linux GOARCH=amd64 go build -o dist/mytool-linux-amd64
GOOS=windows GOARCH=amd64 go build -o dist/mytool-windows-amd64.exe

Testing Across Platforms

Even with AI's help generating cross-platform code, testing remains essential. Consider:

• Using GitHub Actions matrix builds to test on macOS, Ubuntu, and Windows runners
• Running Windows tests in a VM or WSL2 if you're on a Mac
• Leveraging Docker for Linux testing regardless of your host OS
• Using Bridge Terminal to monitor remote test runs across different machines

Real-World Application

Bridge Terminal itself is a cross-platform CLI tool built with AI assistance. The bridge manager runs on macOS, Linux, and Windows, handling WebSocket connections, process management, and terminal streaming identically across all platforms. Platform-specific code is isolated in dedicated files, with build tags ensuring the right code compiles for each target.

Building such a tool traditionally might take weeks of platform research and testing. With AI assistance, the core implementation came together in days, with the AI correctly identifying where platform-specific code was needed and implementing appropriate abstractions.

Conclusion

AI has dramatically lowered the barrier to building robust cross-platform CLI tools. Instead of memorizing platform differences or constantly referencing documentation, you can describe what you want and let the AI handle the platform-specific details. The result is faster development cycles and tools that work correctly from day one on all major operating systems.

The terminal remains a powerful interface for developers. With AI assistance, building tools for that interface is more accessible than ever.