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Course Outline

Introduction to OpenAI Codex CLI

  • Understanding Codex CLI and its 2025 open-source Rust architecture
  • Key features: prompt handling, file operations, bash execution, and multi-step tasks
  • Comparison with Claude Code and other terminal agents
  • Overview of approval modes and security boundaries

Installation and Setup

  • Installing Codex CLI on macOS and Linux
  • 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
  • Reading, writing, and editing files through prompts
  • Running shell commands and processing piped outputs
  • Managing working directories and project context

Approval Modes and Safety

  • Configuring automatic, ask-before-execute, and fully manual modes
  • Sandboxing and distinguishing between read-only and write-enabled sessions
  • Safely handling destructive commands and file deletions

Git and CI Integration

  • Using Codex CLI to generate commits and diffs
  • Implementing pre-commit hooks with agent-based 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
  • Building internal MCP tools for proprietary systems

Multi-Backend Support

  • Switching between OpenAI, Gemini, and GitHub Models APIs
  • Local inference with 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
  • Setting up standardized team prompts and guardrails

Custom Prompts and Workflows

  • Writing reusable prompt templates
  • Chaining tasks for complex refactoring projects
  • Batch processing multiple files and repositories

Performance Tuning

  • Understanding Rust performance characteristics
  • Optimizing token usage for large projects
  • Managing caching and session state

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
  • Accessing community resources and contributing to open-source
  • Transitioning to advanced multi-agent orchestration topics

Requirements

  • Experience in software development using any programming language
  • Basic familiarity with command-line and terminal usage
  • Knowledge of Git fundamentals

Target Audience

  • Software developers interested in incorporating AI terminal agents into their workflows
  • DevOps engineers exploring Rust-based AI tools
  • Team leads assessing OpenAI Codex CLI for organizational adoption
 14 Hours

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