Strong first use : Context and Prompts

Clear prompting makes results more reliable A good default is to include four things in your prompt:

• Goal: What are you trying to change or build?
• Context: Which files, folders, docs, examples, or errors matter for this task? You can @ mention certain files as context.
• Constraints: What standards, architecture, safety requirements, or conventions should Codex follow?
• Done when: What should be true before the task is complete, such as tests passing, behavior changing, or a bug no longer reproducing?

it is similar to Claude Code Best practices

Plan first for difficult tasks

A few approaches work well: Use Plan mode:/plan or Shift + Tab Ask Codex to interview you: when your idea is ambiguous Use a PLANS.md template: use PLANS.md

Make guidance reusable with AGENTS.md

Think of AGENTS.md as an open-format README for agents.

A good AGENTS.md covers:

• repo layout and important directories
• How to run the project
• Build, test, and lint commands
• Engineering conventions and PR expectations
• Constraints and do-not rules
• What done means and how to verify work

I think information above is very important!!!! /init to scaffold a starter AGENTS.md Keep it practical. A short, accurate AGENTS.md is more useful than a long file full of vague rules. If AGENTS.md starts getting too large, keep the main file concise and reference task-specific markdown files for things like planning, code review, or architecture.

Configure Codex for consistency

A good starting pattern is:

• Keep personal defaults in ~/.codex/config.toml (Settings > Configuration > Open config.toml in the ChatGPT desktop app)
• Keep repo-specific behavior in .codex/config.toml
• Use command-line overrides only for one-off situations (if you use the CLI)

Improve reliability with testing and review

Don’t stop at asking Codex to make a change. Ask it to create tests when needed, run the relevant checks, confirm the result, and review the work before you accept it. Codex can do this loop for you, but only if it knows what “good” looks like. That guidance can come from either the prompt or AGENTS.md. /review gives you a few ways to review code

If you and your team have a code_review.md file and reference it from AGENTS.md, Codex can follow that guidance during review as well.

Use MCPs for external context

Use MCP when:

• The needed context lives outside the repo
• The data changes frequently
• You want Codex to use a tool rather than rely on pasted instructions
• You need a repeatable integration across users or projects

codex mcp add command in the CLI to add your custom servers with a name, URL, and other details.

Turn repeatable work into skills

Use a skill to package the instructions in a SKILL.md file

What a skill.md file should include: 1.Keep each skill scoped to one job 2.Start with 2 to 3 concrete use cases 3.define clear inputs and outputs 4.write the description so it says what the skill does and when to use it 5.Include the kinds of trigger phrases a user would actually say

The often situation:

• Log triage
• Release note drafting
• PR review against a checklist
• Migration planning
• Telemetry or incident summaries
• Standard debugging flows

The $skill-creator skill is the best place to start to scaffold the first version of a skill.

Use scheduled tasks for repeated work

Good candidates include:

• Summarizing recent commits
• Scanning for likely bugs
• Drafting release notes
• Checking CI failures
• Producing standup summaries
• Running repeatable analysis workflows on a schedule

Organize long-running chats

The ChatGPT desktop app lets you pin chats and create worktrees. If you use the CLI, these slash commands are especially useful:

• /experimental to toggle experimental features and add to your config.toml
• /resume to resume a saved chat
• /fork to create a new chat while preserving the original transcript
• /compact when the chat is getting long and you want a summarized version of earlier context. Codex also compacts chats automatically
• /agent when you are running parallel agents and want to switch between the active agent thread
• /theme to choose a syntax highlighting theme
• /apps to use ChatGPT apps directly in Codex
• /status to inspect the current session state

Common mistakes

A few common mistakes to avoid when first using Codex:

• Overloading the prompt with durable rules instead of moving them into AGENTS.md or a skill
• Not letting the agent see its work by not giving details on how to best run build and test commands
• Skipping planning on multi-step and complex tasks
• Giving Codex full permission to your computer before you understand the workflow
• Running live tasks on the same files without using Git worktrees
• Scheduling a recurring task before it’s reliable manually
• Treating Codex like something you have to watch step by step instead of using it in parallel with your own work
• Using one chat for an entire project instead of one chat per coherent outcome. This leads to bloated context and worse results over time

用例1:代码理解

识别核心逻辑;梳理服务和板块之间的关系;追踪数据在系统中的流转;识别架构模式和文档确实的部分

用例2:重构和迁移:

跨文件执行修改操作;拆分模块

用例3:性能优化:

用于识别并解决性能瓶颈问题

用例4:提高测试覆盖率

帮助编写测试:边界情况和异常路径

用例5:提高开发速度

提高开发前期和收尾阶段的效率

用例6:保持专注高效

帮助在日常零碎/频繁被打断的情况下保持任务专注从而提高效率

用例7:探索和创意构建

用于开发性工作:给出多种思路,探索陌生开发模式;对各类假设进行验证

最佳实践:

提供清晰的框架,上下文和迭代空间

从"询问模式"开始:

在询问模式下获取可实施方案->在代码模式下将其作为参考依据

持续改进的Codex的开发环境:

配置环境变量,启动脚本以及网络权限

像GitHub Issue一样组织提示:

按照在PR或Issue中描述代码变更的口吻编写提示

将Codex任务队列用作简单的任务清单:

临时新建任务纪录零碎想法,未完成工作或者临时小的更改

使用AGENTS.md文件提供持续上下文:

内容:该文件通常包含仅凭代码无法让 Codex 自行推断的命名规范、业务逻辑、特殊规则以及依赖关系

使用Best of N 提高输出效果:

单个任务一次性生成多份结果,择优选用