📁 Chapter 1: Verifying the Installation

Before using Codex, verify that the installation is working correctly.

Check the Installed Version

codex --version

Explanation
codex launches the Codex CLI executable. --version requests the currently installed version. This command confirms that Codex is installed and accessible through the system PATH.

Example Output:

codex-cli 0.135.0

A successful output indicates that the installation is functioning properly.

📁 Chapter 2: Creating a Test Workspace

Create a dedicated workspace for experimentation.

mkdir -p ~/workspace/test-codex
cd ~/workspace/test-codex

Explanation
mkdir -p creates parent directories if they do not exist. cd changes the current working directory.

📁 Chapter 3: Understanding Existing Code

Create a simple Python program.

cat > calculator.py << 'EOF'
def add(a, b):
 return a + b
print(add(10, 20))
EOF

Then launch Codex: codex and ask: "Explain calculator.py line by line." Expected result: explanation of function, parameters, return, call.

📁 Chapter 4: Refactoring Code

Prompt: "Refactor calculator.py into a proper command line calculator supporting add, subtract, multiply and divide." Codex improves structure, adds operations, validation.

📁 Chapter 5: Feature Enhancement

Create a task manager (todo.py). Then prompt: "Improve this application: save tasks to JSON, load tasks on startup, support delete task, support list task." Codex adds persistence and commands.

cat > todo.py << 'EOF'
tasks = []
while True:
 task = input("Task: ")
 if task == "exit": break
 tasks.append(task)
 print(tasks)
EOF

📁 Chapter 6: Bug Detection

Create buggy file bug.py with index out of range. Prompt: "Find and fix all bugs in bug.py." Codex identifies IndexError and suggests safe access.

numbers = [1,2,3,4,5]
print(numbers[10]) # IndexError

📁 Chapter 7: Automated Code Review

Move into existing project cd path/to/project → codex → prompt: "Review this repository and identify: 1. Security issues 2. Performance issues 3. Code quality issues 4. Maintainability concerns."

📁 Chapter 8: Generating Unit Tests

Prompt: "Create pytest unit tests for all Python modules in this project." Codex analyzes modules and generates test files.

📁 Chapter 9: Architecture Analysis

Powerful use case — Prompt: "Analyze this repository and explain: 1. Architecture 2. Main components 3. Data flow 4. Security risks 5. Performance bottlenecks."

📁 Chapter 10: OCR Project Improvement Example

For an OCR repository: "Analyze all OCR preprocessing steps and propose an implementation plan to improve OCR confidence from 30% to above 70%. Provide: 1. Architecture 2. File changes 3. New dependencies 4. Docker changes 5. Step-by-step implementation plan." This workflow delivers senior architect level insights.

⚙️ Recommended Daily Workflow

• Pull latest source code.
• Start Codex inside the repository.
• Ask Codex to analyze recent changes.
• Request bug detection.
• Generate tests & perform security review.
• Refactor problematic modules.
• Generate implementation plans.
• Review results before applying changes.
• Commit verified changes to Git.

🏁 Conclusion

OpenAI Codex CLI is most effective when used as an AI software engineering assistant rather than a simple code generator. Its greatest value comes from repository understanding, architecture analysis, code review, automated testing, refactoring, debugging, technical planning, and large-scale project assistance.