huggingface-skills

Hugging Face Skills

Hugging Face Skills are definitions for AI/ML tasks like dataset creation, model training, and evaluation. They are interoperable with all major coding agent tools like OpenAI Codex, Anthropic's Claude Code, Google DeepMind's Gemini CLI, and Cursor.

The Skills in this repository follow the standardized format Agent Skill format.

How do Skills work?

In practice, skills are self-contained folders that package instructions, scripts, and resources together for an AI agent to use on a specific use case. Each folder includes a SKILL.md file with YAML frontmatter (name and description) followed by the guidance your coding agent follows while the skill is active.

[!NOTE] 'Skills' is actually an Anthropic term used within Claude AI and Claude Code and not adopted by other agent tools, but we love it! OpenAI Codex uses an AGENTS.md file to define the instructions for your coding agent. Google Gemini uses 'extensions' to define the instructions for your coding agent in a gemini-extension.json file. This repo is compatible with all of them, and more!

[!TIP] If your agent doesn't support skills, you can use agents/AGENTS.md directly as a fallback.

Installation

Hugging Face skills are compatible with Claude Code, Codex, and Gemini CLI. With integrations Cursor, Windsurf, and Continue, on the way.

Claude Code

1. Register the repository as a plugin marketplace:

/plugin marketplace add huggingface/skills

2. To install a skill, run:

/plugin install <skill-name>@huggingface/skills

For example:

/plugin install hugging-face-cli@huggingface/skills

Codex

1. Codex will identify the skills via the AGENTS.md file. You can verify the instructions are loaded with:

codex --ask-for-approval never "Summarize the current instructions."

2. For more details, see the Codex AGENTS guide.

Gemini CLI

1. This repo includes gemini-extension.json to integrate with the Gemini CLI.

2. Install locally:

gemini extensions install . --consent

or use the GitHub URL:

gemini extensions install https://github.com/huggingface/skills.git --consent

4. See Gemini CLI extensions docs for more help.

Skills

This repository contains a few skills to get you started. You can also contribute your own skills to the repository.

Available skills

Name	Description	Documentation
hugging-face-cli	Execute Hugging Face Hub operations using the hf CLI. Download models/datasets, upload files, manage repos, and run cloud compute jobs.	SKILL.md
hugging-face-datasets	Create and manage datasets on Hugging Face Hub. Supports initializing repos, defining configs/system prompts, streaming row updates, and SQL-based dataset querying/transformation.	SKILL.md
hugging-face-evaluation	Add and manage evaluation results in Hugging Face model cards. Supports extracting eval tables from README content, importing scores from Artificial Analysis API, and running custom evaluations with vLLM/lighteval.	SKILL.md
hugging-face-jobs	Run compute jobs on Hugging Face infrastructure. Execute Python scripts, manage scheduled jobs, and monitor job status.	SKILL.md
hugging-face-model-trainer	Train or fine-tune language models using TRL on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes hardware selection, cost estimation, Trackio monitoring, and Hub persistence.	SKILL.md
hugging-face-paper-publisher	Publish and manage research papers on Hugging Face Hub. Supports creating paper pages, linking papers to models/datasets, claiming authorship, and generating professional markdown-based research articles.	SKILL.md
hugging-face-tool-builder	Build reusable scripts for Hugging Face API operations. Useful for chaining API calls or automating repeated tasks.	SKILL.md
hugging-face-trackio	Track and visualize ML training experiments with Trackio. Log metrics via Python API and retrieve them via CLI. Supports real-time dashboards synced to HF Spaces.	SKILL.md

Using skills in your coding agent

Once a skill is installed, mention it directly while giving your coding agent instructions: