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Course Outline
Introduction to OpenAI Codex CLI
- Overview of Codex CLI and its 2025 open-source Rust architecture
- Core features: prompt processing, file operations, bash execution, and multi-step tasks
- Comparison with Claude Code and other terminal agents
- Safety boundaries and overview of approval modes
Installation and Setup
- Installing Codex CLI on macOS and Linux systems
- Configuring API keys for OpenAI and compatible providers
- Connecting to local backends via Ollama and Atomic Chat
- Setting up SSH and remote development environments
Core Workflow Commands
- Executing single prompts and multi-turn sessions
- Performing file read, write, and edit operations via prompts
- Running shell commands and handling piped outputs
- Managing working directories and project context
Approval Modes and Safety
- Configuring automatic, ask-before-execute, and fully manual modes
- Sandboxing techniques: read-only versus write-enabled sessions
- Safely handling destructive commands and file deletions
Git and CI Integration
- Generating commits and diffs using Codex CLI
- Implementing pre-commit hooks with agent-based code review
- Running Codex CLI in headless CI environments
- Integrating with GitHub Actions and GitLab CI
MCP Server Integration
- Connecting to Model Context Protocol servers
- Extending tool capabilities with custom MCP endpoints
- Developing internal MCP tools for proprietary systems
Multi-Backend Support
- Switching between OpenAI, Gemini, and GitHub Models APIs
- Local inference using Ollama and self-hosted endpoints
- Strategies for model selection based on latency versus quality
Team Deployment and Governance
- Managing shared configurations and secrets
- Establishing usage policies and audit logging for enterprise needs
- Setting up standardized team prompts and guardrails
Custom Prompts and Workflows
- Creating reusable prompt templates
- Chaining tasks for complex refactoring projects
- Batch processing multiple files and repositories
Performance Tuning
- Understanding Rust's performance characteristics
- Optimizing token usage for large-scale projects
- Caching mechanisms and session state management
Troubleshooting Common Issues
- Resolving connection failures to backends
- Debugging prompt ambiguity and misinterpretations
- Handling rate limiting and implementing retry strategies
Security Best Practices
- Protecting API keys in shared environments
- Preventing prompt injection and command hijacking
- Addressing data residency and compliance considerations
Summary and Next Steps
- Recap of core capabilities and workflows
- Community resources and open-source contribution opportunities
- Transitioning to advanced multi-agent orchestration topics
Requirements
- Experience in software development using any programming language
- Familiarity with basic command-line and terminal usage
- Understanding of Git fundamentals
Audience
- Software developers aiming to incorporate AI terminal agents into their workflow
- DevOps engineers exploring Rust-based AI tools
- Team leads assessing OpenAI Codex CLI for organizational adoption
14 Hours
