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# Entry
OpenAI Codex it can do much more than generating snippets of code or making minor changes. If properly configured, he can behave more like a forceful software engineer – one who follows instructions carefully, understands context, effectively uses command-line interface (CLI) tools and workflows, makes coordinated changes to multiple files, and checks his own work before submitting it.
In this article, I’ll discuss five practical ways to make Codex more effective in real coding work. Instead of treating it as a basic code generation tool, the goal is to employ it more like an artificial intelligence (AI) coding agent that can analyze longer tasks, keep pace with the design, and generate more reliable results.
Note: These are my own views and some people may approach the Code differently. That said, the ideas in this article are not based solely on personal opinions. They are shaped by recent research articles, official OpenAI guidelines, and broader patterns and practices emerging in the vibration coding community.
# 1. Using planning mode to lend a hand the Code accomplish long-term tasks
OpenAI recommends using Plan mode for tasks that are complicated, ambiguous, or arduous to describe well because it allows Codex to gather context, ask clarifying questions, and build a more solid plan before it starts making changes. OpenAI’s guidance also highlights that when it’s arduous to break down a task, asking Codex to propose a plan is often the best way to structure the work.
In practice, this changes the quality of interaction. Instead of jumping straight into code generation, Codex first tries to understand the problem, check the available context, and arrange the task into a clearer sequence of steps. This makes it much better suited for long-term work, where success depends less on producing a single block of code and more on managing sequencing, constraints, checkpoints, and validation as part of a larger workflow.
# 2. Using the AGENTS.md file for design rules and memory management
The AGENTS.md the file is not just a quick Code review file. This is one of the best ways to define design rules, workflows, tooling expectations, and other work instructions that Codex can employ when working in the codebase. The OpenAI documentation says Codex reads AGENTS.md files before doing any work, and its CLI can even generate a skeleton /init which you can refine and engage in future sessions.
This is where AGENTS.md becomes particularly useful in practice. It helps Codex understand how your project works, what tools and skills are available, and what standards it should follow. It also supports memory management, not as ChatGPT-style personal memory, but as a lightweight project memory layer.
OpenAI’s long-term guidelines explicitly rely on persistent files for markdown plans, execution instructions, and documentation, and Codex also supports resuming saved sessions. Together, these features provide a more hard-wearing way to transfer context between longer tasks and different sessions.
# 3. Create custom code skills for reusable coding processes
Skills are one of the most useful ways to expand the Codex beyond a single prompt. OpenAI describes them as reusable bundles of instructions, scripts and resources, packaged around: SKILL.md file so they can codify domain-specific repeatable workflows, conventions, and processes. Codex supports these skills across the application, CLI, and integrated development environment (IDE) extensions.
The Code also includes built-in system skills such as $skill-creator AND $skill-installerthat make it easier to scaffold and install up-to-date skills locally. This becomes especially useful when your workflow is unique. Instead of just relying on generic, built-in behaviors, you can create custom skills that teach Codex how to handle project-specific tasks, external tools, internal application programming interfaces (APIs), or repeatable publishing flows.
For my own website and article workflow, these skills save a lot of time: they allow Codex to apply structured formatting, employ CLI tools, and interact with external services in a much more reliable and repeatable way.
# 4. Conducting the Code test, verifying and validating its results
This becomes even more useful with GPT-5.4. The newer model is designed for stronger coding and longer, multi-step workflows, and official guidelines highlight features such as verification loops, clear completion checks, and better employ of tools for complicated tasks. In tiny, it’s better not to stop at the first answer and be more willing to test it until the result is correct.
In practice, this means Codex can write code, run tests, check the website and user interface (UI), check whether the result actually meets requirements, make corrections, and repeat until the task is done correctly. For best results, explicitly ask him to check his own work: tell him to run the tests, open the app, review the UI, verify the behavior on the page, and continue refining the results until everything works as expected.
# 5. Using shell tools to transform OpenAI code into a real coding agent
Shell tools are one of the easiest ways to make Codex feel like a real coding agent, not just a code generator. The current Codex CLI and IDE workflows are based on this idea: Codex can read files, make edits, and run commands in your project, and the hint guide even recommends a shell tool for terminal commands. This matters because a lot of real engineering work is already done in CLIs, whether that’s GitHub ghimplementation with Vercelor other local tools that connect the codebase to external systems.
What I like most is that this often eliminates the need to overcomplicate things with additional Model Context Protocol (MCP) servers or custom skills. You can simply ask Codex to employ the CLI tools that are already part of your workflow. This usually means fewer tokens, faster execution, and a setup much closer to your normal local development environment. This allows more of your workflow to rely on the tools you already trust, rather than moving everything to a different layer of abstraction.
# Final thoughts
I employ OpenAI Codex in VS Code almost every day, for both personal and professional projects. It gets better over time and sometimes I really feel like a “fraud” when I ask him to fix something and he solves the problem within minutes. However, a large part of getting to this point is not just using the Code, but learning how to work with it properly.
What made the biggest difference for me was consistently following a few basic practices: giving better instructions, carefully managing context, using planning mode before major changes, and building custom skills for repeatable project workflows. I also ask him to verify his own work by running tests, checking the page or user interface using tools like Playwrightand the employ of CLI and shell tools to directly interact with the local environment and external systems. This setup reduces friction, speeds things up, and makes the overall workflow feel much closer to working with a real coding agent.
Abid Ali Awan (@1abidaliawan) is a certified data science professional who loves building machine learning models. Currently, he focuses on creating content and writing technical blogs about machine learning and data science technologies. Abid holds a Master’s degree in Technology Management and a Bachelor’s degree in Telecommunications Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.