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Give your agents the power of the Hugging Face ecosystem

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Give your agents the power of the Hugging Face ecosystem

Hugging Face Skills

Hugging Face Skills are definitions for AI/ML tasks like dataset creation, model training, and evaluation. The client plugin marketplaces expose the hf-cli skill as the bootstrap path for core Hub operations; additional workflow skills can be installed on demand with hf skills add <skill-name> or discovered by skill-aware clients over CLI/MCP integrations.

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

[!NOTE] Just want to give your agent access to the Hugging Face Hub? Start with hf-cli. It's the recommended first Skill to install: it teaches your agent every hf command (search models, manage datasets and buckets, launch Spaces, run jobs) and is generated from your locally installed CLI so it stays current. 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 agentsmd/AGENTS.md directly as a fallback.

The hf-cli skill in this repository is 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:

  2. Install the CLI skill:

  3. To install another Hugging Face skill, use the hf CLI:
    Codex

  4. 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.

  5. 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.

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

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

  8. Install locally:

or use the GitHub URL:

  1. 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. The marketplace entry is intentionally limited to hf-cli; use hf skills add <skill-name> to install additional workflow skills.

For contributors, regenerate manifests with: 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 Hugging Face Hub CLI (hf) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. SKILL.md
hf-cloud-aws-context-discovery Discover the user''s local AWS context (active profile, region, account ID, caller identity) at the start of any AWS task. SKILL.md
hf-cloud-python-env-setup Set up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3. SKILL.md
hf-cloud-sagemaker-deployment-planner Plan and coordinate the deployment of a model to Amazon SageMaker AI. SKILL.md
hf-cloud-sagemaker-iam-preflight Ensure a usable SageMaker execution role exists before deploying or training. SKILL.md
hf-cloud-sagemaker-production-defaults Create a SageMaker endpoint (real-time, real-time scale-to-zero, or async) with autoscaling, CloudWatch alarms, and tagging enabled by default. SKILL.md
hf-cloud-serving-image-selection Pick the right serving container for a SageMaker model deployment and find its current image URI. SKILL.md
hf-mem Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights

Issues· 61 open

View all issuesOpen on GitHub
  • #263

    Add skills to awesome-ai-plugins?

    Updated Sep 25, 2026
  • #243

    hf-cli skill pipes an installer into a shell; heads-up on an audit publishing September 8

    Updated Sep 3, 2026
  • #240

    AI Skill Shield scan report: huggingface/skills (26 skills)

    Updated Aug 30, 2026
  • #214

    测试探索1

    Updated Jul 29, 2026
  • #194

    Docs idea: quick hosted trial for hf-mcp

    Updated Jul 7, 2026
  • #173

    huggingface-llm-trainer: Approach 4 example drops required `push_to_hub` configuration

    Updated Jun 16, 2026
  • #163

    TODO -- Reconcile hf-llm-trainer with new trl auto skill

    Updated Jun 8, 2026
  • #121

    Critical: AGENTS.md missing YAML frontmatter (Update overrides manual fixes)

    Updated Apr 20, 2026
  • #107

    Add a description to improve Dispatch discoverability

    Updated Mar 29, 2026
  • #79

    Failures during install.

    Updated Mar 7, 2026

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> Details

PublishedAug 1, 2026
UpdatedOct 2, 2026
Category编程语言
PricingOpen source

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