Codex Best Practices

中文 | English

Summary

This source gives the clearest high-level operating model for Codex: structure task context up front, move durable rules into AGENTS.md and config layers, verify through tests and review, then turn stable workflows into MCP-backed skills or automations.

Source

• Raw file: raw/openai/codex/Best practices – Codex.md
• Translated raw file: raw/openai/codex/Best practices – Codex.zh.md
• Original URL: https://developers.openai.com/codex/learn/best-practices
• Ingest date: 2026-04-09

Key Contributions

• Defines the four-part prompt structure: goal, context, constraints, and done-when criteria.
• Pushes ambiguous work toward planning surfaces such as plan mode, interview-style clarification, or execution-plan templates.
• Treats AGENTS.md as the durable repo-local instruction surface and warns against repeating stable guidance in every prompt.
• Connects reliability to testing, review, MCP integrations, skills, automations, and explicit session management.

Strongest Claims

• Codex quality is often a workflow-design problem rather than a prompting-only problem.
• Repeated manual steering should be migrated into durable config, repo docs, skills, or automations instead of left in chat history.
• High-permission and high-autonomy modes should be adopted only after the workflow is understood and validated.

Related Pages

• Codex Operating Practices
• Agent-First Repositories
• Agent Harness Design
• Wiki Overview