OpenAI Codex CLI Settings and Custom Prompts

A curated collection of configurations and custom prompts for OpenAI Codex CLI, designed to enhance your development workflow with various model providers and reusable prompt templates.

For Claude Code settings, agents and custom commands, please refer feiskyer/claude-code-settings.

Overview

This repository provides:

• Flexible Configuration: Support for multiple model providers (LiteLLM, GitHub Copilot, DeepSeek, Ollama)
• Custom Prompts: Reusable prompt templates for common development tasks
• Best Practices: Pre-configured settings optimized for development workflows
• Easy Setup: Simple installation and configuration process

Quick Start

Installation

# Backup existing Codex configuration (if any)
mv ~/.codex ~/.codex.bak

# Clone this repository to ~/.codex
git clone https://github.com/feiskyer/codex-settings.git ~/.codex

# Or symlink if you prefer to keep it elsewhere
ln -s /path/to/codex-settings ~/.codex

Basic Configuration

The default config.toml uses LiteLLM as a gateway. To use it:

1. Install LiteLLM and Codex CLI:

   pip install -U 'litellm[proxy]'
   npm install -g @openai/codex

2. Create a LiteLLM config file (full example litellm_config.yaml):

   general_settings:
   master_key: sk-dummy
   litellm_settings:
   drop_params: true
   model_list:
   - model_name: gpt-5
   litellm_params:
      model: github_copilot/gpt-5
      extra_headers:
         editor-version: "vscode/1.85.1"           # Editor version
         editor-plugin-version: "copilot/1.155.0"  # Plugin version
         Copilot-Integration-Id: "vscode-chat"     # Integration ID
         user-agent: "GithubCopilot/1.155.0"       # User agent

3. Start LiteLLM proxy:

   litellm --config ~/.codex/litellm_config.yaml
   # Runs on http://localhost:4000 by default

4. Run Codex:

   codex

Configuration Files

Main Configuration

• config.toml: Default configuration using LiteLLM gateway
  • Model: gpt-5
  • Approval policy: on-request
  • Reasoning: High effort with detailed summaries

Alternative Configurations

Located in configs/ directory:

• OpenAI ChatGPT: Use ChatGPT subscription provider
• Azure OpenAI: Use Azure OpenAI service provider
• Github Copilot: Use Github Copilot via LiteLLM proxy
• OpenRouter: Use OpenRouter provider
• Model Scope: Use ModelScope provider
• Kimi: Use Moonshot Kimi provider

To use an alternative config:

# Take ChatGPT for example
cp ~/.codex/configs/chatgpt.toml config.toml
codex

Custom Prompts

Custom prompts are stored in the prompts/ directory. Access them via the /prompts: slash menu in Codex.

Github Spec Kit Workflow

Github Spec Kit - Unified interface for Spec-Driven Development.

To use it, run the following command to initialize your project:

uvx --from git+https://github.com/github/spec-kit.git specify init .

Alternatively, you can also copy .specify to your project root directory.

Available commands:

• /prompts:constitution - Create or update governing principles and development guidelines.
• /prompts:specify - Define requirements and user stories for the desired outcome.
• /prompts:clarify - Resolve underspecified areas (run before /prompts:plan unless explicitly skipped).
• /prompts:plan - Generate a technical implementation plan for the chosen stack.
• /prompts:tasks - Produce actionable task lists for implementation.
• /prompts:analyze - Check consistency and coverage after /prompts:tasks and before /prompts:implement.
• /prompts:implement - Execute all tasks to build the feature according to the plan.

Kiro Spec Workflow

Kiro Workflow - Complete feature development from spec to execution. The Kiro commands provide a structured workflow for feature development:

1. /prompts:kiro-spec-creator [feature] - Create requirements and acceptance criteria
2. /prompts:kiro-feature-designer [feature] - Develop architecture and component design
3. /prompts:kiro-task-planner [feature] - Generate implementation task lists
4. /prompts:kiro-task-executor [feature] [task] - Execute specific implementation tasks
5. /prompts:kiro-assistant [question] - Quick development assistance

Other Prompts

• /prompts:deep-reflector - Capture session retrospectives, user preferences, and follow-up actions.
• /prompts:insight-documenter - Record technical breakthroughs and update the breakthroughs index.
• /prompts:instruction-reflector - Audit and refine the AGENTS.md instructions for Codex.
• /prompts:github-issue-fixer 1234 - Run the full GitHub issue workflow from planning through PR creation.
• /prompts:github-pr-reviewer 1234 - Conduct structured PR reviews focused on actionable findings.
• /prompts:ui-engineer [requirements] - Deliver or review frontend implementations with modern UI standards.
• /prompts:prompt-creator [requirements] - Scaffold new Codex prompts following best-practice structure.

Creating Custom Prompts

1. Create a new .md file in ~/.codex/prompts/
2. Use argument placeholders:
   • $1 to $9: Positional arguments
   • $ARGUMENTS: All arguments joined by spaces
   • $$: Literal dollar sign
3. Restart Codex to load new prompts

Configuration Options

Approval Policies

• untrusted: Prompt for untrusted commands (recommended)
• on-failure: Only prompt when sandbox commands fail
• on-request: Model decides when to ask
• never: Auto-approve all commands (use with caution)

Sandbox Modes

• read-only: Can read files, no writes or network
• workspace-write: Can write to workspace, network configurable
• danger-full-access: Full system access (use in containers only)

Reasoning Settings

For reasoning-capable models (o3, gpt-5):

• Effort: minimal, low, medium, high
• Summary: auto, concise, detailed, none

Shell Environment

Control which environment variables are passed to subprocesses:

[shell_environment_policy]
inherit = "all"  # all, core, none
exclude = ["AWS_*", "AZURE_*"]  # Exclude patterns
set = { CI = "1" }  # Force-set values

Advanced Features

Profiles

Define multiple configuration profiles:

[profiles.fast]
model = "gpt-4o-mini"
approval_policy = "never"
model_reasoning_effort = "low"

[profiles.reasoning]
model = "o3"
approval_policy = "on-failure"
model_reasoning_effort = "high"

Use with: codex --profile reasoning

MCP Servers

Extend Codex with Model Context Protocol servers:

[mcp_servers.context7]
command = "npx"
args = ["-y", "@upstash/context7-mcp@latest"]

[mcp_servers.claude]
command = "claude"
args = ["mcp", "serve"]

Project Documentation

Codex automatically reads AGENTS.md files in your project to understand context. Please always create one in your project root with /init command on your first codex run.

References

• Codex CLI Official Docs
• Codex GitHub Repository
• LiteLLM Documentation

Contributing

Contributions welcome! Feel free to:

• Add new custom prompts
• Share alternative configurations
• Improve documentation
• Report issues and suggest features

LICENSE

This project is released under MIT License - See LICENSE for details.