Nine Tips and Best Practices for Using Codex Efficiently

Starting the Journey

If you're new to Codex or Vibe Coding, this article will help you get better results faster. It covers the core habits that make Codex more effective in the CLI, the IDE extension and the ChatGPT desktop app, from prompting and planning to verification, MCP, skills and scheduled tasks.

Of course, these tips aren't only useful with Codex; they're general-purpose, and you can adapt and apply them sensibly in other coding-agent tools, such as ClaudeCode, Grok Build, Pi and so on.

Context and Prompting

Today's models are already powerful enough, and things are different from the GPT 5.3 era. Back then we still had to study the principles of prompting and learn all kinds of prompt techniques to get the most out of AI, but model capabilities have since leapt forward by several levels, and even an imperfect prompt can be put to use — sometimes a single sentence is enough to get a very satisfying result.

Although a well-specified prompt is no longer a necessary condition for getting the best value, it really does make results more reliable, especially in ever-larger codebases or high-risk tasks.

If you're maintaining a large, complex project, a best practice is to give Codex the right background and a clear structure for the task.

• Goal: What do you want to change or build?

• Context: Which files, folders, docs, examples or errors matter for this task? You can use "@" to mention certain files as context for the task.

• Constraints: Which standards, architecture, security requirements or conventions should Codex follow?

• Done when: What should be achieved before the task is complete, such as passing specified tests, a behavior change, a clear UI verification, or a bug that no longer reproduces.

This helps Codex keep a clear scope, make fewer assumptions, and produce work that is easier to maintain.

Choosing the reasoning level according to task difficulty not only minimizes token consumption but may also let the task finish faster.

• Low: Suits quicker, simple, well-scoped small tasks.

• Medium or High: For complex adjustments or debugging.

• Extra High: Suits long, complex tasks that need deep reasoning.

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In general, if you're doing fairly ordinary static web development, basic writing, or data processing, a medium reasoning level is enough; if you're building a project with a front end and back end, a desktop application, or complex, fine-grained financial data processing, I recommend raising the reasoning level to High or above.

For Hard Tasks, Plan First

If a task is complex, vague, or hard to describe right away, don't hesitate: plan the task before you start writing code. Here are several ways to plan a task:

1. Codex's built-in planning mode: For most users this is the simplest and most effective option. Plan mode lets Codex gather context, ask clarifying questions, and draw up a complete plan before implementation. Use "/plan" to turn on plan mode.

2. Talk it through: If you have only a rough idea of what you want and don't know how to describe it, have a conversation with codex about your thinking first and let codex take the lead in asking questions. Tell it your idea, your assumptions, even a flash of inspiration, and together turn the vague into the concrete before you start writing code.

3. Use a PLANS.md template: For more advanced workflows, you can configure Codex to follow PLANS.md or an execution-plan template, to handle long-running or multi-step work.

AGENTS.md

AGENTS.md plays a role similar to CLAUDE.md in Claude and can be seen as a README for the AI. It is loaded into the context automatically, and is a very important practice in Vibe Coding.

A good AGENTS.md may contain:

• The repository structure and important directories.

• How to start and run the project.

• Build, test and agent-check commands and scripts.

• Engineering conventions and pull-request requirements.

• Constraints and forbidden rules.

• The definition of "done" and the verification requirements for task results.

The /init slash command in the CLI is a quick-start command for generating an initial AGENTS.md file in the current directory. It's a good starting point, but you should modify the generated content to match how your team actually builds, tests, reviews and ships code.

You can create AGENTS.md files at different levels: a global AGENTS.md under ~/.codex stores personal defaults, repository-level files store shared standards, and more specific files in subdirectories store local rules. If a more specific file exists near the current directory, that file's settings take precedence.

Keep it practical. A short, precise AGENTS.md is more useful than a file full of vague rules. Start from the basics and add new rules only after you notice repeated mistakes.

If AGENTS.md gets too large, keep the main file concise and refer to task-specific files, such as ones for planning, code review or architecture.

A short, precise AGENTS.md kept practical is more efficient and useful than an AGENTS.md with a long string of vague rules. In general, keep this file to around 300 lines. Besides the project's basic information, for mistakes that recur during development, we can put the verified solutions in as entries in AGENTS.

Configure Codex for Consistency

Configuration is one of the main ways to make Codex behave more consistently across sessions and devices. For example, you can specify defaults for model selection, reasoning effort, sandbox mode, approval policy, profiles and MCP settings.

A decent starting configuration is:

• Keep personal defaults in ~/.codex/config.toml (in the ChatGPT desktop app, reach it via "Settings" > "Configuration" > "Open config.toml")

• Keep repository-specific behavior configuration in .codex/config.toml

• Use command-line overrides only in special cases

The config.toml file defines persistent configuration such as MCP servers, multi-agent settings and feature toggles. Overrides for specific profiles are stored in a separate $CODEX_HOME/profile-name.config.toml file.

Codex comes with operation-level sandboxing and offers two key controls for you to adjust. The approval mode decides when Codex asks for your permission to run commands; the sandbox mode decides whether Codex can read and write in a directory, and which files the agent can access.

If you're new to Vibe Coding, I recommend starting with the default permissions. By default, keep approvals and sandbox settings strict, and loosen permissions for trusted repositories or specific workflows only when clearly needed.

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Configure Codex for your real environment early. Many quality problems are actually configuration problems, such as a wrong working directory, missing write permission, wrong model defaults, or missing tools and connectors.

Improve Reliability Through Tests and Review

Don't just ask Codex to push the task forward. Also ask it to create test cases when needed, run the relevant checks, confirm the results, and review the work before you accept it. Codex can complete this loop for you, but only if it knows what the "correct result" should look like. That guidance can come from the prompt or from the AGENTS.md file.

This may include:

• Writing or updating test cases for the change

• Running the corresponding test suite

• Checking code style, formatting or type checks

• Confirming that the final behavior matches the requirements

• Reviewing the diff files for defects, regressions or high-risk patterns

In the ChatGPT desktop app, toggle the "Diff" panel to view changes locally right there. Click a specific line to give feedback, which is fed in as context to Codex's next generation.

A useful option here is the /review slash command, which offers several ways to review code:

• In a PR style, against a base branch

• Review uncommitted changes

• Review a specific commit

• Use custom review instructions

If you and your team have a code_review.md file and reference it in AGENTS.md, Codex can follow that guidance when reviewing too. This is a strong pattern for teams that want review behavior to stay consistent across repositories and contributors.

[!IMPORTANT]

Codex shouldn't just generate code. With the right instructions, it can also help test, check and review code.

Use MCP

Use MCP when the context you need lives outside the repository. It lets Codex connect to the tools and systems you already use, so you don't have to keep copying and pasting live information into prompts.

The Model Context Protocol (MCP for short) is an open standard for connecting Codex to external tools and systems.

Use MCP when:

• The context you need lives outside the repository

• The data changes frequently

• You want Codex to use tools instead of relying on pasted instructions

• You need repeatable integrations across users or projects

Codex supports both STDIO and Streamable HTTP servers with OAuth. In the ChatGPT desktop app, go to "Settings" > "MCP Servers" to see custom servers and recommended servers. Usually, Codex can help you install the servers you need — you just have to ask. You can also use the codex mcp add command in the command-line interface (CLI) to add a custom server by specifying a name, URL and other details.

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Only add a tool when it genuinely improves your workflow. Don't connect every tool you use from the start. Begin with one or two tools that clearly remove a manual step you do often, and expand from there.

Turn Repeatable Work into Skills

Once a workflow can be repeated, stop relying on long prompts or repeated Q&A. Use Skills to package instructions into a SKILL.md file, and make sure the context and the helper logic in Codex are applied consistently. Skills work across the command-line interface (CLI), the IDE extension and the ChatGPT desktop app.

Make each skill target just one task. Start with 2 to 3 concrete use cases, define the inputs and outputs clearly, and write a description that says what the skill does and when to use it. Include the kinds of trigger phrases users would actually say.

Don't try to cover every edge case at the start. Begin with one representative task, make sure it runs well, then turn that workflow into a skill and refine it from there. Include scripts or extra resources only when they improve reliability.

A good rule of thumb: if you keep reusing the same prompt or correcting the same workflow, it should probably become a skill.

Skills are especially useful for repetitive tasks like these:

• Log triage

• Drafting release notes

• Reviewing PRs against a checklist

• Migration planning

• Telemetry or incident summaries

• Standard debugging procedures

The $skill-creator skill is the best starting point for building the first version of a skill. While iterating, keep the first version local. When you're ready to share it widely, package it as a plugin. One of the most important parts of a skill is its description. It should state what the skill does and when to use it.

Personal skills are stored in $HOME/.agents/skills, and shared team skills can be committed to the .agents/skills directory inside the repository. This is especially useful when onboarding new team members.

Use Scheduled Tasks for Recurring Work

Once a workflow runs stably, you can schedule Codex to run it for you automatically in the background. In the ChatGPT desktop app, the scheduled tasks feature lets you pick the project, prompt, frequency and execution environment for periodic work.

Create a scheduled task on the "Schedule" page. Choose the project, the prompt and the frequency, and specify whether the task runs in a dedicated Git worktree or in your local environment.

Good candidate tasks include:

• Summarizing recent commits

• Scanning for possible bugs

• Drafting release notes

• Checking continuous integration (CI) failures

• Writing a daily stand-up summary

• Running repeatable analysis workflows on a schedule

A useful principle: skills decide the method, and scheduled tasks decide the timetable. If a workflow still needs a lot of manual intervention, turn it into a skill first. Once the workflow becomes predictable, putting it on a schedule saves time.

Manage Long-Running Chats

Chats accumulate context, decisions and actions over time, so managing them well has a major impact on quality. The ChatGPT desktop app lets you pin chats and create worktrees. If you use the command-line interface (CLI), the following slash commands are especially useful:

• /experimental toggles experimental features and adds them to the config.toml file

• /resume: resume a saved chat

• /fork: create a new chat while keeping the original conversation record

• /compact: use it when a chat gets too long and you need a summary of the earlier context. Codex also compacts chats automatically

• /agent: use it when you're running multiple parallel agents and want to switch between active agent threads

• /theme: choose a syntax-highlighting theme

• /apps: use ChatGPT apps directly inside Codex

• /status shows the current session status

Keep one chat window per coherent unit of work. If the work still belongs to the same problem, it's usually best to stay in the same chat window, because that preserves the reasoning trail. Fork only when the work genuinely branches.

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Use Codex's subagent workflow to split bounded work off from the main thread. Let the main agent focus on the core problem, and hand tasks such as exploration, testing or triage to subagents.