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 Agent Skills 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.

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

The skills in this repository are also available through:

• Cursor Marketplace (https://cursor.com/marketplace/huggingface)
• Codex Plugins Directory (https://developers.openai.com/codex/plugins)

Installation

Hugging Face skills are compatible with Claude Code, Codex, Gemini CLI, and Cursor.

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 hf-cli@huggingface/skills

Codex

1. Copy or symlink any skills you want to use from this repository's skills/ directory into one of Codex's standard .agents/skills locations (for example, $REPO_ROOT/.agents/skills or $HOME/.agents/skills) as described in the Codex Skills guide.

2. Once a skill is available in one of those locations, Codex will discover it using the Agent Skills standard and load the SKILL.md instructions when it decides to use that skill or when you explicitly invoke it.

3. If your Codex setup still relies on AGENTS.md, you can use the generated agents/AGENTS.md file in this repo as a fallback bundle of instructions.

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.

Cursor

This repository includes Cursor plugin manifests:

• .cursor-plugin/plugin.json
• .mcp.json (configured with the Hugging Face MCP server URL)

Install from repository URL (or local checkout) via the Cursor plugin flow.

For contributors, regenerate manifests with:

./scripts/publish.sh

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
hf-cli	Execute Hugging Face Hub operations using the hf CLI. Download models/datasets, upload files, manage repos, and run cloud compute jobs.	SKILL.md
huggingface-community-evals	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
huggingface-datasets	Explore, query, and extract data from any Hugging Face dataset using the Dataset Viewer REST API and npx tooling. Zero Python dependencies — covers split/config discovery, row pagination, text search, filtering, SQL via parquetlens, and dataset upload via CLI.	SKILL.md
huggingface-gradio	Build Gradio web UIs and demos in Python. Use when creating or editing Gradio apps, components, event listeners, layouts, or chatbots.	SKILL.md
huggingface-llm-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
huggingface-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