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name description license metadata
codex-cli-specialist
This skill should be used when the user asks to "set up Codex CLI", "convert skills for Codex", "write cross-platform AI skills", "configure agents/openai.yaml", "build skills index", "validate skill compatibility", "sync skills between Claude Code and Codex", or "optimize Codex CLI workflows". Use for OpenAI Codex CLI mastery, cross-platform skill authoring, skill conversion, and multi-agent compatibility patterns.
MIT + Commons Clause
version category domain
1.0.0
engineering
development-tools

Codex CLI Specialist

Expert-level guidance for OpenAI Codex CLI: installation, configuration, skill authoring, cross-platform compatibility with Claude Code, and productivity workflows.

Keywords

codex, codex-cli, openai, skill authoring, agents/openai.yaml, cross-platform skills, claude code, skill conversion, skill index, multi-agent, ai cli tools, developer productivity, codex configuration, skill management

Table of Contents

Quick Start

# Install Codex CLI
npm install -g @openai/codex

# Verify installation
codex --version

# Convert an existing Claude Code skill to Codex format
python scripts/codex_skill_converter.py path/to/SKILL.md --output-dir ./converted

# Validate a skill works on both Claude Code and Codex
python scripts/cross_platform_validator.py path/to/skill-dir

# Build a skills index from a directory of skills
python scripts/skills_index_builder.py /path/to/skills --output skills-index.json

Tools Overview

1. Codex Skill Converter

Converts a Claude Code SKILL.md into Codex-compatible format by generating an agents/openai.yaml configuration and restructuring metadata.

Input: Path to a Claude Code SKILL.md file Output: Codex-compatible skill directory with agents/openai.yaml

Usage:

# Convert a single skill
python scripts/codex_skill_converter.py my-skill/SKILL.md

# Specify output directory
python scripts/codex_skill_converter.py my-skill/SKILL.md --output-dir ./codex-skills/my-skill

# JSON output for automation
python scripts/codex_skill_converter.py my-skill/SKILL.md --json

What it does:

  • Parses YAML frontmatter from SKILL.md
  • Extracts name, description, and metadata
  • Generates agents/openai.yaml with proper schema
  • Copies scripts, references, and assets
  • Reports conversion status and any warnings

2. Cross-Platform Validator

Validates that a skill directory is compatible with both Claude Code and Codex CLI environments.

Input: Path to a skill directory Output: Validation report with pass/fail status and recommendations

Usage:

# Validate a skill directory
python scripts/cross_platform_validator.py my-skill/

# Strict mode - treat warnings as errors
python scripts/cross_platform_validator.py my-skill/ --strict

# JSON output
python scripts/cross_platform_validator.py my-skill/ --json

Checks performed:

  • SKILL.md exists and has valid YAML frontmatter
  • Required frontmatter fields present (name, description)
  • Description uses third-person format for auto-discovery
  • agents/openai.yaml exists and is valid YAML
  • scripts/ directory contains executable Python files
  • No external dependencies beyond standard library
  • File structure matches expected patterns

3. Skills Index Builder

Builds a skills-index.json manifest from a directory of skills, useful for skill registries and discovery systems.

Input: Path to a directory containing skill subdirectories Output: JSON manifest with skill metadata

Usage:

# Build index from skills directory
python scripts/skills_index_builder.py /path/to/skills

# Custom output file
python scripts/skills_index_builder.py /path/to/skills --output my-index.json

# Human-readable output
python scripts/skills_index_builder.py /path/to/skills --format human

# Include only specific categories
python scripts/skills_index_builder.py /path/to/skills --category engineering

Output includes:

  • Skill name, description, version
  • Available scripts and tools
  • Category and domain classification
  • File counts and sizes
  • Platform compatibility flags

Core Workflows

Workflow 1: Install and Configure Codex CLI

Step 1: Install Codex CLI

# Install globally via npm
npm install -g @openai/codex

# Verify installation
codex --version
codex --help

Step 2: Configure API access

# Set your OpenAI API key
export OPENAI_API_KEY="sk-..."

# Or configure via the CLI
codex configure

Step 3: Choose an approval mode and run

# suggest (default) - you approve each change
codex --approval-mode suggest "refactor the auth module"

# auto-edit - auto-applies file edits, asks before shell commands
codex --approval-mode auto-edit "add input validation"

# full-auto - fully autonomous (use in sandboxed environments)
codex --approval-mode full-auto "set up test infrastructure"

Workflow 2: Author a Codex Skill from Scratch

Step 1: Create directory structure

mkdir -p my-skill/agents
mkdir -p my-skill/scripts
mkdir -p my-skill/references
mkdir -p my-skill/assets

Step 2: Write SKILL.md with compatible frontmatter

---
name: my-skill
description: This skill should be used when the user asks to "do X",
  "perform Y", or "analyze Z". Use for domain expertise, automation,
  and best practice enforcement.
license: MIT + Commons Clause
metadata:
  version: 1.0.0
  category: engineering
  domain: development-tools
---

# My Skill

Description and workflows here...

Step 3: Create agents/openai.yaml

# Use the template from assets/openai-yaml-template.yaml
name: my-skill
description: >
  Expert guidance for X, Y, and Z.
instructions: |
  You are an expert at X. When the user asks about Y,
  follow these steps...
tools:
  - name: my_tool
    description: Runs the my_tool.py script
    command: python scripts/my_tool.py

Step 4: Add Python tools

# Create your script
touch my-skill/scripts/my_tool.py
chmod +x my-skill/scripts/my_tool.py

Step 5: Validate the skill

python cross_platform_validator.py my-skill/

Workflow 3: Convert Claude Code Skills to Codex

Step 1: Identify skills to convert

# List all skills in a directory
find engineering-team/ -name "SKILL.md" -type f

Step 2: Run the converter

# Convert a single skill
python scripts/codex_skill_converter.py engineering-team/code-reviewer/SKILL.md \
  --output-dir ./codex-ready/code-reviewer

# Batch convert (shell loop)
for skill_md in engineering-team/*/SKILL.md; do
  skill_name=$(basename $(dirname "$skill_md"))
  python scripts/codex_skill_converter.py "$skill_md" \
    --output-dir "./codex-ready/$skill_name"
done

Step 3: Review and adjust generated openai.yaml

The converter generates a baseline agents/openai.yaml. Review it for:

  • Accuracy of the instructions field
  • Completeness of the tools list
  • Correct command paths for scripts

Step 4: Validate the converted skill

python scripts/cross_platform_validator.py ./codex-ready/code-reviewer

Workflow 4: Validate Cross-Platform Compatibility

# Run validator on a skill (outputs PASS/WARN/FAIL for each check)
python scripts/cross_platform_validator.py my-skill/

# Strict mode (warnings become errors)
python scripts/cross_platform_validator.py my-skill/ --strict --json

The validator checks both Claude Code compatibility (SKILL.md, frontmatter, scripts) and Codex CLI compatibility (agents/openai.yaml, tool references), plus cross-platform checks (UTF-8 encoding, skill size, name consistency).

Workflow 5: Build and Publish a Skills Index

# Build index from a directory of skills
python scripts/skills_index_builder.py ./engineering-team --output skills-index.json

# Human-readable summary
python scripts/skills_index_builder.py ./engineering-team --format human

Codex CLI Configuration Deep Dive

agents/openai.yaml Structure

The agents/openai.yaml file is the primary configuration for Codex CLI skills. It tells Codex how to discover, describe, and invoke the skill.

# Required fields
name: skill-name                    # Unique identifier (kebab-case)
description: >                      # What the skill does (for discovery)
  Expert guidance for X. Analyzes Y and generates Z.

# Instructions define the skill's behavior
instructions: |
  You are a senior X specialist. When the user asks about Y:
  1. First, analyze the context
  2. Then, apply framework Z
  3. Finally, produce output in format W

  Always follow these principles:
  - Principle A
  - Principle B

# Tools expose scripts to the agent
tools:
  - name: tool_name                 # Tool identifier (snake_case)
    description: >                  # When to use this tool
      Analyzes X and produces Y report
    command: python scripts/tool.py # Execution command
    args:                           # Optional: define accepted arguments
      - name: input_path
        description: Path to input file
        required: true
      - name: output_format
        description: Output format (json or text)
        required: false
        default: text

# Optional metadata
model: o4-mini                      # Preferred model
version: 1.0.0                     # Skill version

Skill Discovery and Locations

Codex CLI discovers skills from these locations (in priority order):

  1. Project-local: .codex/skills/ in the current working directory
  2. User-global: ~/.codex/skills/ for user-wide skills
  3. System-wide: /usr/local/share/codex/skills/ (rare, admin-managed)
  4. Registry: Remote skills index (when configured)

Precedence rule: Project-local overrides user-global overrides system-wide.

# Install a skill locally to a project
cp -r my-skill/ .codex/skills/my-skill/

# Install globally for all projects
cp -r my-skill/ ~/.codex/skills/my-skill/

Invocation Patterns

# Direct invocation by name
codex --skill code-reviewer "review the latest PR"

# Codex auto-discovers relevant skills from context
codex "analyze code quality of the auth module"

# Chain with specific approval mode
codex --approval-mode auto-edit --skill senior-fullstack \
  "scaffold a Next.js app with GraphQL"

# Pass files as context
codex --skill code-reviewer --file src/auth.ts "review this file"

Cross-Platform Skill Patterns

Shared Structure Convention

A skill that works on both Claude Code and Codex CLI follows this layout:

my-skill/
├── SKILL.md              # Claude Code reads this (primary documentation)
├── agents/
│   └── openai.yaml       # Codex CLI reads this (agent configuration)
├── scripts/              # Shared - both platforms execute these
│   ├── tool_a.py
│   └── tool_b.py
├── references/           # Shared - knowledge base
│   └── guide.md
└── assets/               # Shared - templates and resources
    └── template.yaml

Key insight: SKILL.md and agents/openai.yaml serve the same purpose (skill definition) for different platforms. The scripts/, references/, and assets/ directories are fully shared.

Frontmatter Compatibility

Claude Code and Codex use different frontmatter fields. A cross-platform SKILL.md should include all relevant fields:

---
# Claude Code fields (required)
name: my-skill
description: This skill should be used when the user asks to "do X"...

# Extended metadata (optional, used by both)
license: MIT + Commons Clause
metadata:
  version: 1.0.0
  category: engineering
  domain: development-tools

# Codex-specific hints (optional, ignored by Claude Code)
codex:
  model: o4-mini
  approval_mode: suggest
---

Dual-Target Skill Layout

When writing instructions in SKILL.md, structure them so they work regardless of platform:

  1. Use standard markdown - both platforms parse markdown well
  2. Reference scripts by relative path - scripts/tool.py works everywhere
  3. Show both invocation patterns - document Claude Code natural language and Codex CLI command-line usage side by side

Skill Installation and Management

Installing Skills Locally

# Clone a skill into your project
git clone https://github.com/org/skills-repo.git /tmp/skills
cp -r /tmp/skills/code-reviewer .codex/skills/code-reviewer

# Or use a git submodule for version tracking
git submodule add https://github.com/org/skills-repo.git .codex/skills-repo

Managing and Versioning Skills

# List installed skills
ls -d .codex/skills/*/

# Update all skills from source
cd .codex/skills-repo && git pull origin main

Use skills-index.json for version pinning across team members. The index builder tool generates this manifest automatically.

Integration Points

Syncing Skills Between Claude Code and Codex

Strategy 1: Shared repository (recommended) - Keep all skills in one repo with both SKILL.md and agents/openai.yaml. Both platforms read from the same source.

Strategy 2: CI/CD conversion - Maintain Claude Code skills as source of truth. Use a GitHub Actions workflow that triggers on **/SKILL.md changes to auto-run codex_skill_converter.py and commit the generated agents/openai.yaml files.

Strategy 3: Git hooks - Add a pre-commit hook that detects modified SKILL.md files and regenerates agents/openai.yaml automatically before each commit.

CI/CD for Skill Libraries

Add a validation workflow that runs cross_platform_validator.py --strict --json on all skill directories during push/PR, and uses skills_index_builder.py to generate and upload an updated skills-index.json artifact.

GitHub-Based Skill Distribution

# Tag, build index, and create release
git tag v1.0.0 && git push origin v1.0.0
python skills_index_builder.py . --output skills-index.json
gh release create v1.0.0 skills-index.json --title "Skills v1.0.0"

Best Practices

Skill Authoring

  1. Keep descriptions discovery-friendly - Use third-person, keyword-rich descriptions that start with "This skill should be used when..."
  2. One skill, one concern - Each skill should cover a coherent domain, not an entire discipline
  3. Scripts use standard library only - No pip install requirements for core functionality
  4. Include both SKILL.md and agents/openai.yaml - Makes the skill usable on any platform immediately
  5. Test scripts independently - Every Python tool should work standalone via python script.py --help

Codex CLI Usage

  1. Start with suggest mode - Use --approval-mode suggest until you trust the skill
  2. Scope skill contexts narrowly - Pass specific files with --file instead of entire directories
  3. Use project-local skills - Avoid global installation for project-specific skills
  4. Pin versions in teams - Use skills-index.json for version consistency across team members
  5. Review generated configs - Always review auto-generated agents/openai.yaml before deploying

Cross-Platform Compatibility

  1. Relative paths everywhere - Scripts reference scripts/, references/, assets/ with relative paths
  2. No shell-specific syntax - Avoid bash-isms in scripts; stick to Python for portability
  3. Standard YAML only - No YAML extensions or anchors that might confuse parsers
  4. UTF-8 encoding - All files should be UTF-8 encoded
  5. Unix line endings - Use LF, not CRLF (configure .gitattributes)

Performance

  1. Keep skills small - Under 1MB total for fast loading and distribution
  2. Minimize reference files - Include only essential knowledge, not entire docs
  3. Lazy-load expensive tools - Split heavy scripts into separate files
  4. Cache tool outputs - Use --json output for piping into other tools

Reference Documentation

Resource Location Description
Codex CLI Guide references/codex-cli-guide.md Installation, configuration, features
Cross-Platform Skills references/cross-platform-skills.md Multi-agent compatibility guide
openai.yaml Template assets/openai-yaml-template.yaml Ready-to-use Codex config template

Common Patterns Quick Reference

Pattern: Quick Skill Conversion

# One-liner: convert and validate
python scripts/codex_skill_converter.py skill/SKILL.md && \
  python scripts/cross_platform_validator.py skill/

Pattern: Batch Validation

# Validate all skills in a directory
for d in */; do
  [ -f "$d/SKILL.md" ] && python scripts/cross_platform_validator.py "$d"
done

Pattern: Generate Index for Registry

python scripts/skills_index_builder.py . --output skills-index.json --format json

Pattern: Codex Quick Task

# Run a quick task with a skill
codex --approval-mode auto-edit --skill codex-cli-specialist \
  "convert all skills in engineering-team/ to Codex format"

Pattern: Minimal Codex Skill

# agents/openai.yaml - absolute minimum
name: my-skill
description: Does X for Y
instructions: You are an expert at X. Help the user with Y.

Pattern: Full-Featured Codex Skill

See the complete production-grade template at assets/openai-yaml-template.yaml, which includes instructions, tools, model selection, and versioning.