Detailed explanation of command-line tools
Learn how to use command-line AI tools, including Claude Code, Codex CLI, Gemini CLI, and others, and understand terminal AI enhancement workflows and automation capabilities.
Claude Code: terminal-integrated AI assistant
Claude Code is a command-line AI tool from Anthropic, deeply integrated with the terminal environment and supporting MCP and GitHub workflow automation.
Terminal integration
- • Seamless integration: Use directly in the terminal without switching apps
- • Context awareness: Understand the current directory and Git status
- • Command execution：AI can execute Shell commands and view the results
- • File operations: read, create, modify files
MCP configuration
Claude Code supports MCP (Model Context Protocol) and can connect to external services:
- • File system MCP: access the local file system
- • GitHub MCP: GitHub API integration, managing repositories and Issues
- • Database MCP: connect to the database and query data
- • Custom MCP: Create your own MCP Server
GitHub workflow automation
- • Issue management: Create, update, and close issues
- • PR actions: Create Pull Request, code review
- • Automation scripts: Batch-process GitHub tasks
- • Workflow integration: work with GitHub Actions
Codex CLI & Gemini CLI: multi-model CLI tools
Master the use of Codex CLI and Gemini CLI to enable multi-model switching and project analysis.
Codex CLI
- • GPT-5.1 Codex: Call the OpenAI Codex model
- • Monorepo analysis: Analyze large Monorepo projects
- • /init command: automatically generate project rules and configurations
- • Code generation: generate code based on project context
Gemini CLI & Droid
- • Batch script execution: Batch processing files and tasks
- • Document automation: Automatically organize and generate documentation
- • Multi-model switching: Seamlessly switch between Claude/Gemini/GPT
- • Droid mode: Android development-only feature
Tips for switching between multiple models
- • Task assignment: choose the model based on task characteristics (Claude reasoning, GPT code, Gemini multimodal)
- • Cost optimization: use lightweight models for simple tasks, powerful models for complex tasks
- • Parallel calls: Call multiple models at the same time and compare the results
- • Fallback mechanism：Automatically switch to a backup model when the primary model fails
Warp & Continue.dev: AI-enhanced terminal
Understand AI-enhanced terminal tools and improve command-line work efficiency.
Warp terminal
- • AI command error correction: Automatically correct command errors
- • Natural language to Shell: Describe in natural language and generate Shell commands
- • Command history search: AI-enhanced command search
- • Smart completion: context-aware command completion
Continue.dev
- • Local model configuration: integrate Ollama and use local models
- • Enterprise private knowledge base: Connect to the internal enterprise knowledge base
- • Multi-model support: Supports multiple AI models
- • Background agent workflow: Execute long-running tasks in the background
Local model configuration (Ollama)
- • Model download: Use Ollama to download local models (Llama, Mistral, etc.)
- • API service: Start the Ollama API service
- • Privacy protection: code is not uploaded to the cloud
- • Cost control: Completely free, no API call fees
Goose: open-source Agent automation
Goose is an open-source AI Agent framework focused on automating script writing and execution.
Core features
- • Open source and free: Fully open source, free to use and modify
- • Agent mode: autonomously execute complex tasks
- • Command-line interface: a simple CLI interface
- • Script generation: Automatically generate and execute scripts
Use cases
- • Batch file processing: Rename, convert, and organize files
- • Code refactoring: automated code refactoring tasks
- • Data migration: database migration, data transformation
- • Deployment automation: Automated deployment process
Tool comparison and workflows
Choose the appropriate command-line tools for the scenario and build an efficient workflow.
Scenario selection
Daily development
Claude Code (terminal integration, MCP support)
Large-scale project analysis
Codex CLI (monorepo analysis, rule generation)
Privacy-sensitive projects
Continue.dev + Ollama (local model, code not uploaded)
Automation scripts
Goose (open-source Agent, script generation)
Workflow recommendations
- • Development phase: Use Claude Code for day-to-day development, connect to GitHub via MCP
- • Project analysis: Codex CLI analyzes large projects and generates project rules
- • Batch processing: Gemini CLI batch processes files and documents
- • Automation: Writing and executing automation scripts with Goose
Learning outcomes
After completing this chapter, you will:
- 1Learn to use command-line AI tools (Claude Code, Codex CLI, Gemini CLI)
- 2Able to configure MCP and local models (Ollama), and understand terminal AI-augmented workflows
- 3Master multi-model switching techniques and choose the appropriate model based on the scenario
- 4Understand the features and use cases of tools such as Warp, Continue.dev, and Goose