# What Are the Different Types of Skills Available in OpenAI? A Complete Guide > Explore OpenAI skills: System, Curated, and Experimental. Discover the full range of reusable capabilities available in this comprehensive guide. - Repository: [OpenAI/skills](https://github.com/openai/skills) - Tags: tutorial - Published: 2026-02-16 --- **OpenAI’s skills repository organizes reusable capabilities into three distinct categories: System skills, Curated skills, and Experimental skills.** The `openai/skills` repository provides a framework for packaging prompts, scripts, and configurations into modular units called **skills**. Understanding the different types of skills available in OpenAI is essential for developers who want to extend Codex or build custom agent capabilities. Each skill type serves a specific purpose, from core system functionality to production-ready automations and early-stage prototypes. ## The Three Types of Skills Available in OpenAI OpenAI categorizes skills based on stability, support level, and installation requirements. The repository structure in `skills/` reflects this taxonomy through specific subdirectories. ### System Skills **System skills** reside in `skills/.system/` and provide foundational functionality required for Codex to operate. These skills are pre-installed automatically and cannot be removed by end users. The most critical system skill is the **skill-installer** itself, located at `skills/.system/skill-installer/`. This skill enables the discovery and installation of other skill types. Key implementation files include: - [`SKILL.md`](https://github.com/openai/skills/blob/main/SKILL.md) – Defines the skill’s metadata and human-readable description - [`scripts/list-skills.py`](https://github.com/openai/skills/blob/main/scripts/list-skills.py) – Scans curated directories and outputs available skills - [`scripts/install-skill-from-github.py`](https://github.com/openai/skills/blob/main/scripts/install-skill-from-github.py) – Handles download and installation logic using git sparse checkout System skills follow the same directory structure as other types but are protected from user modification to ensure platform stability. ### Curated Skills **Curated skills** represent production-ready, recommended capabilities stored in `skills/.curated/`. These skills undergo review and maintenance by OpenAI, making them the preferred choice for most use cases. Users install curated skills on demand using the `$skill-installer` command. Notable examples include: - **yeet** – Automates Git workflows (stage, commit, push) with intelligent message generation - **openai-docs** – Generates documentation based on OpenAI’s style guidelines Each curated skill contains: - [`SKILL.md`](https://github.com/openai/skills/blob/main/SKILL.md) with front-matter metadata (`name`, `description`, `metadata`) - [`agents/openai.yaml`](https://github.com/openai/skills/blob/main/agents/openai.yaml) defining the agent prompt template and configuration - `scripts/` directory with executable helpers (Python, Bash, etc.) - Optional `references/` and `assets/` for documentation and visual resources ### Experimental Skills **Experimental skills** occupy `skills/.experimental/` when present, though this directory may not exist in all repository tags. These skills represent early-stage prototypes, beta features, or community contributions that have not yet achieved curated status. Installation requires explicit path specification: ```bash $skill-installer --path skills/.experimental/my-skill ``` Experimental skills follow identical structural conventions to curated skills but may contain unstable APIs, incomplete documentation, or breaking changes between commits. The [`README.md`](https://github.com/openai/skills/blob/main/README.md) at the repository root documents the availability and risks of experimental skills. ## How OpenAI Skills Are Structured Regardless of type, every skill adheres to a standardized directory layout that enables automatic discovery and execution. This consistency allows the `$skill-installer` to handle System, Curated, and Experimental skills uniformly. ### Core Components - **[`SKILL.md`](https://github.com/openai/skills/blob/main/SKILL.md)** – The entry point containing YAML front-matter with `name`, `description`, and optional metadata. The installer parses this file to display human-readable information in the skill list. - **[`agents/openai.yaml`](https://github.com/openai/skills/blob/main/agents/openai.yaml)** – Configures the AI agent’s behavior, including system prompts, temperature settings, and tool specifications. Codex loads this configuration when invoking the skill. - **`scripts/`** – Contains executable files that implement the skill’s functionality. These may be Python scripts (like [`list-skills.py`](https://github.com/openai/skills/blob/main/list-skills.py)), Bash scripts (like [`deploy.sh`](https://github.com/openai/skills/blob/main/deploy.sh)), or other automation tools. - **`references/`** – Supporting documentation, API specifications, or code snippets that provide context for the skill’s operation. - **`assets/`** – Images, SVGs, or other media files used in documentation or UI rendering within Codex. ### Installation Process When you execute `$skill-installer `, the system performs the following operations as implemented in [`install-skill-from-github.py`](https://github.com/openai/skills/blob/main/install-skill-from-github.py): 1. Validates the skill exists in `skills/.curated/` or the specified path 2. Performs a git sparse checkout to download only the skill’s directory 3. Copies the skill folder to `$CODEX_HOME/skills/` 4. Validates the presence of required files ([`SKILL.md`](https://github.com/openai/skills/blob/main/SKILL.md), [`agents/openai.yaml`](https://github.com/openai/skills/blob/main/agents/openai.yaml)) System skills bypass this process since they are bundled with the Codex installation. ## Working with Skills: Practical Examples The following examples demonstrate how to interact with the different types of skills available in OpenAI using the command-line tools and Python SDK. ### Listing Available Skills To view all curated skills that can be installed: ```bash $skill-installer list ``` This command executes [`list-skills.py`](https://github.com/openai/skills/blob/main/list-skills.py), which scans `skills/.curated/` and extracts metadata from each [`SKILL.md`](https://github.com/openai/skills/blob/main/SKILL.md) file. The output displays skill names and descriptions in a numbered list. ### Installing a Curated Skill To install the **yeet** skill for Git automation: ```bash $skill-installer yeet ``` For experimental skills, specify the full path: ```bash $skill-installer --path skills/.experimental/my-prototype ``` Behind the scenes, this invokes [`install-skill-from-github.py`](https://github.com/openai/skills/blob/main/install-skill-from-github.py), which handles authentication via `GITHUB_TOKEN` if accessing private repositories, performs sparse checkout, and installs dependencies if specified in the skill configuration. ### Invoking Skills Programmatically Once installed, skills integrate with the Codex Python SDK: ```python from codex import Agent agent = Agent(model="gpt-4o-mini") response = agent.run( """ Use the **yeet** skill to stage, commit, and push a change. Description: "fix typo in README" """ ) print(response) ``` The agent loads the skill’s [`agents/openai.yaml`](https://github.com/openai/skills/blob/main/agents/openai.yaml) configuration from `$CODEX_HOME/skills/yeet/` to determine the system prompt and available tools. ## Summary OpenAI’s skills repository organizes capabilities into three distinct categories: - **System skills** (`skills/.system/`) provide core platform functionality like the skill-installer itself and come pre-installed with Codex - **Curated skills** (`skills/.curated/`) represent production-ready, recommended tools such as **yeet** and **openai-docs** that users install on demand - **Experimental skills** (`skills/.experimental/`) offer early-stage prototypes and beta features requiring explicit path specification during installation All skill types follow a standardized structure centered on [`SKILL.md`](https://github.com/openai/skills/blob/main/SKILL.md) metadata and [`agents/openai.yaml`](https://github.com/openai/skills/blob/main/agents/openai.yaml) configuration, enabling consistent discovery via [`list-skills.py`](https://github.com/openai/skills/blob/main/list-skills.py) and installation via [`install-skill-from-github.py`](https://github.com/openai/skills/blob/main/install-skill-from-github.py). ## Frequently Asked Questions ### What is the difference between system skills and curated skills in OpenAI? System skills are pre-installed core components stored in `skills/.system/` that provide essential platform functionality like the skill-installer itself. Curated skills reside in `skills/.curated/` and are optional, production-ready tools that users must explicitly install using `$skill-installer `. ### How do I install an experimental skill from the OpenAI skills repository? Experimental skills require explicit path specification since they reside in `skills/.experimental/`. Use the command `$skill-installer --path skills/.experimental/` to install them. Note that this directory may not exist in all repository tags, and these skills may contain unstable APIs. ### What files are required to create a custom skill for OpenAI? Every skill requires a [`SKILL.md`](https://github.com/openai/skills/blob/main/SKILL.md) file containing YAML front-matter with `name` and `description` fields, and an [`agents/openai.yaml`](https://github.com/openai/skills/blob/main/agents/openai.yaml) file defining the agent configuration and prompts. Optional directories include `scripts/` for executables, `references/` for documentation, and `assets/` for media files. ### Can I use the OpenAI skills system with private repositories? Yes, the skill-installer supports private repositories by respecting the `GITHUB_TOKEN` environment variable. When set, [`install-skill-from-github.py`](https://github.com/openai/skills/blob/main/install-skill-from-github.py) uses this token for authentication during the git sparse checkout process, allowing access to private skills repositories.