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OpenAI Codex CLI Settings and Custom Prompts

A curated collection of configurations, skills and custom prompts for [OpenAI Codex CLI](https://github.com/openai/codex), designed to enhance your development workflow with various model providers and reusable prompt templates.

For Claude Code settings, skills, agents and custom commands, please refer [feiskyer/claude-code-settings](https://github.com/feiskyer/claude-code-settings).

Overview

This repository provides:

**Flexible Configuration**: Support for multiple model providers (LiteLLM/Copilot proxy, ChatGPT subscription, Azure OpenAI, OpenRouter)
**Custom Prompts**: Reusable prompt templates for common development tasks
**Skills (Experimental)**: Discoverable instruction bundles for specialized tasks (image generation, YouTube transcription, spec-driven workflows)
**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

Using npx skills

`npx skills` could be used to install skills only for your AI coding tools.

# List skills
npx -y skills add -l feiskyer/codex-settings

# Install all skills
npx -y skills add --all feiskyer/codex-settings

# Manually select a list of skills to install
npx -y skills add feiskyer/codex-settings

Basic Configuration

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

Install LiteLLM and Codex CLI:

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

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

   general_settings:
     master_key: sk-dummy
   litellm_settings:
     drop_params: true
   model_list:
   - model_name: gpt-5.1-codex-max
     model_info:
       mode: responses
       supports_vision: true
     litellm_params:
       model: github_copilot/gpt-5.1-codex-max
       drop_params: true
       extra_headers:
         editor-version: "vscode/1.95.0"
         editor-plugin-version: "copilot-chat/0.26.7"
   - model_name: claude-opus-4.5
     litellm_params:
       model: github_copilot/claude-opus-4.5
       drop_params: true
       extra_headers:
         editor-version: "vscode/1.95.0"
         editor-plugin-version: "copilot-chat/0.26.7"
   - model_name: "*"
     litellm_params:
       model: "github_copilot/*"
       extra_headers:
         editor-version: "vscode/1.95.0"
         editor-plugin-version: "copilot-chat/0.26.7"

Start LiteLLM proxy:

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

Run Codex:

   codex

Configuration Files

Main Configuration

[config.toml](config.toml): Default configuration using LiteLLM gateway
Model: `gpt-5` via `model_provider = "github"` (Copilot proxy on `http://localhost:4000`)
Approval policy: `on-request`; reasoning summary: `detailed`; reasoning effort: `high`; raw agent reasoning visible
MCP servers: `claude` (local), `exa` (hosted), `chrome` (DevTools over `npx`)

Alternative Configurations

Located in `configs/` directory:

[OpenAI ChatGPT](configs/chatgpt.toml): Use ChatGPT subscription provider
[Azure OpenAI](configs/azure.toml): Use Azure OpenAI service provider
[Github Copilot](configs/github-copilot.toml): Use Github Copilot via LiteLLM proxy
[OpenRouter](configs/openrouter.toml): Use OpenRouter provider

To use an alternative config:

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

Custom Prompts

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

`/prompts:deep-reflector` - Analyze development sessions to extract learnings, patterns, and improvements for future interactions.
`/prompts:insight-documenter [breakthrough]` - Capture and document significant technical breakthroughs into reusable knowledge assets.
`/prompts:instruction-reflector` - Analyze and improve Codex instructions in AGENTS.md based on conversation history.
`/prompts:github-issue-fixer [issue-number]` - Systematically analyze, plan, and implement fixes for GitHub issues with PR creation.
`/prompts:github-pr-reviewer [pr-number]` - Perform thorough GitHub pull request code analysis and review.
`/prompts:ui-engineer [requirements]` - Create production-ready frontend solutions with modern UI/UX standards.
`/prompts:prompt-creator [requirements]` - Create Codex custom prompts with proper structure and best practices.

Creating Custom Prompts

Create a new `.md` file in `~/.codex/prompts/`
Use argument placeholders:

`$1` to `$9`: Positional arguments
`$ARGUMENTS`: All arguments joined by spaces
`$`: Literal dollar sign

Restart Codex to load new prompts

Skills (Experimental)

Skills are reusable instruction bundles that Codex automatically discovers at startup. Each skill has a name, description, and detailed instructions stored on disk. Codex injects only metadata (name, description, path) into context - the body stays on disk until needed.

How to Use Skills

Skills are automatically loaded when Codex starts. To use a skill:

**List all skills**: Use the `/skills` command to see all available skills

   /skills

**Invoke a skill**: Use `
lt;skill-name> [prompt]` to invoke a skill with an optional prompt

   $kiro-skill Create a feature spec for user authentication
   $nanobanana-skill Generate an image of a sunset over mountains

Skills are stored in `~/.codex/skills/**/SKILL.md`. Only files named exactly `SKILL.md` are recognized.

Available Skills

<details> <summary>claude-skill - Handoff task to Claude Code CLI</summary>

[claude-skill](skills/claude-skill)

Non-interactive automation mode for hands-off task execution using Claude Code. Use when you want to leverage Claude Code to implement features or review code.

**Key Features:**

Multiple permission modes (default, acceptEdits, plan, bypassPermissions)
Autonomous execution without approval prompts
Streaming progress updates
Structured final summaries

**Requirements:** Claude Code CLI installed (`npm install -g @anthropic-ai/claude-code`)

</details>

<details> <summary>autonomous-skill - Long-running task automation</summary>

[autonomous-skill](skills/autonomous-skill)

Execute complex, long-running tasks across multiple sessions using a dual-agent pattern (Initializer + Executor) with automatic session continuation.

The runner keeps model selection with your active Codex config/profile and pins unattended execution through config overrides rather than hardcoding a model or relying on `--full-auto`.

**Key Features:**

Dual-agent pattern (Initializer creates task list, Executor completes tasks)
Auto-continuation across sessions with progress tracking
Task isolation with per-task directories (`.autonomous/<task-name>/`)
Progress persistence via `task_list.md` and `progress.md`
Non-interactive mode execution

**Usage:**

# Start a new autonomous task
~/.codex/skills/autonomous-skill/scripts/run-session.sh "Build a REST API for todo app"

# Continue an existing task
~/.codex/skills/autonomous-skill/scripts/run-session.sh --task-name build-rest-api-todo --continue

# List all tasks
~/.codex/skills/autonomous-skill/scripts/run-session.sh --list

</details>

<details> <summary>deep-research - Multi-agent deep research orchestration</summary>

[deep-research](skills/deep-research)

Multi-instance (multi-agent) orchestration workflow for deep research tasks. Breaks down research objectives into parallelizable sub-goals, runs child processes via `codex exec`, and aggregates results into polished reports.

**Key Features:**

Parallel sub-task execution with `codex exec` in sandboxed environments
Automatic aggregation and chapter-by-chapter refinement
Structured output with deliverable reports (not chat messages)
Progress tracking with detailed logs per sub-task
Tool priority: skills → MCP (`firecrawl` → `tavily`) → direct fetch

**Use Cases:**

Systematic web/document research
Competitor/industry analysis
Batch link/dataset retrieval
Long-form writing with evidence integration

**Workflow:**

Pre-execution planning & reconnaissance
Sub-goal identification and task decomposition
Generate dispatch scripts with parallel execution
Design child process prompts
Parallel execution with monitoring
Programmatic aggregation into base draft
Interpret results and design structure
Chapter-by-chapter refinement
Final delivery as standalone file

**Output:** All artifacts saved to `.research/<name>/` directory including logs, raw data, and final polished report.

</details>

<details> <summary>nanobanana-skill - Image generation with Gemini</summary>

[nanobanana-skill](skills/nanobanana-skill)

Generate or edit images using Google Gemini API via nanobanana. Use when creating, generating, or editing images.

**Key Features:**

Image generation with various aspect ratios (square, portrait, landscape, ultra-wide)
Image editing capabilities
Multiple model options (gemini-3-pro-image-preview, gemini-2.5-flash-image)
Resolution options (1K, 2K, 4K)

**Requirements:**

`GEMINI_API_KEY` configured in `~/.nanobanana.env`
Python3 with google-genai, Pillow, python-dotenv

</details>

<details> <summary>youtube-transcribe-skill - Extract YouTube subtitles</summary>

[youtube-transcribe-skill](skills/youtube-transcribe-skill)

Extract subtitles/transcripts from a YouTube video URL and save as a local file.

**Key Features:**

Dual extraction methods: CLI (`yt-dlp`) and Browser Automation (fallback)
Automatic subtitle language selection (zh-Hans, zh-Hant, en)
Cookie handling for age-restricted content
Saves transcripts to local text files

**Requirements:**

`yt-dlp` (for CLI method), or
Browser automation MCP server (for fallback method)

</details>

<details> <summary>kiro-skill - Interactive feature development</summary>

[kiro-skill](skills/kiro-skill)

Interactive feature development workflow from idea to implementation. Creates requirements (EARS format), design documents, and implementation task lists.

**Triggered by:** "kiro" or references to `.kiro/specs/` directory

**Workflow:**

**Requirements** → Define what needs to be built (EARS format with user stories)
**Design** → Determine how to build it (architecture, components, data models)
**Tasks** → Create actionable implementation steps (test-driven, incremental)
**Execute** → Implement tasks one at a time

**Storage:** Creates files in `.kiro/specs/{feature-name}/` directory

</details>

<details> <summary>spec-kit-skill - Constitution-based development</summary>

[spec-kit-skill](skills/spec-kit-skill)

GitHub Spec-Kit integration for constitution-based spec-driven development.

**Triggered by:** "spec-kit", "speckit", "constitution", "specify", or references to `.specify/` directory

**Prerequisites:**

# Install spec-kit CLI
uv tool install specify-cli --from git+https://github.com/github/spec-kit.git

# Initialize project
specify init . --ai codex

**7-Phase Workflow:**

**Constitution** → Establish governing principles
**Specify** → Define functional requirements
**Clarify** → Resolve ambiguities (max 5 questions)
**Plan** → Create technical strategy
**Tasks** → Generate dependency-ordered tasks
**Analyze** → Validate consistency (read-only)
**Implement** → Execute implementation

</details>

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.openrouter]
model = "gpt-5"
model_reasoning_effort = "high"
approval_policy = "on-request"
sandbox_mode = "workspace-write"
model_provider = "openrouter"

[profiles.github]
model = "gpt-5"
model_reasoning_effort = "high"
approval_policy = "on-request"
sandbox_mode = "workspace-write"
model_provider = "github"

[model_providers.github]
name     = "OpenAI"
base_url = "http://localhost:4000"
http_headers = { "Authorization"= "Bearer sk-dummy" }
wire_api = "responses"

[model_providers.openrouter]
name     = "OpenRouter"
base_url = "https://openrouter.ai/api/v1"
http_headers = { "Authorization"= "Bearer [YOUR-API-KEY]"}
wire_api = "responses"

Use with: `codex --profile openrouter`

MCP Servers

Extend Codex with Model Context Protocol servers:

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

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](https://developers.openai.com/codex/cli/)
[Codex GitHub Repository](https://github.com/openai/codex)
[LiteLLM Documentation](https://docs.litellm.ai/)

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](LICENSE) for details.

OpenAI Codex CLI Settings and Custom Prompts

Markdown

OpenAI Codex CLI Settings and Custom Prompts

Overview

Quick Start

Installation

Using npx skills

Basic Configuration

Configuration Files

Main Configuration

Alternative Configurations

Custom Prompts

Creating Custom Prompts

Skills (Experimental)

How to Use Skills

Available Skills

[claude-skill](skills/claude-skill)

[autonomous-skill](skills/autonomous-skill)

[deep-research](skills/deep-research)

[nanobanana-skill](skills/nanobanana-skill)

[youtube-transcribe-skill](skills/youtube-transcribe-skill)

[kiro-skill](skills/kiro-skill)

[spec-kit-skill](skills/spec-kit-skill)

Configuration Options

Approval Policies

Sandbox Modes

Reasoning Settings

Shell Environment

Advanced Features

Profiles

MCP Servers

Project Documentation

References

Contributing

LICENSE

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