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huggingface-skills

A platform for hosting, sharing and running open-source machine learning models, datasets and demos.

From plugin
skills
11k26 skills1 MCP

Claude Code

> /plugin marketplace add huggingface/skills
> /plugin install hf-cli@huggingface-skills

Security report

Found

Low risk

  • Known flaws

    Not published

  • Signed build

    Not published

  • Maintained

    Oct 30, 2026

    Last push

  • Licence

    Apache-2.0

  • Runs as

    Remote

    huggingface.co

  • Deprecated

    Not published

Signals from public registries, not an audit. Checked Nov 11, 2026.

skills README

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:

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
  1. Install the CLI skill:
/plugin install hf-cli@huggingface/skills
  1. To install another Hugging Face skill, use the hf CLI:
hf skills add <skill-name>

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 agentsmd/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
  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:

./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

NameDescriptionDocumentation
hf-cliHugging 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-discoveryDiscover 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-setupSet up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3.SKILL.md
hf-cloud-sagemaker-deployment-plannerPlan and coordinate the deployment of a model to Amazon SageMaker AI.SKILL.md
hf-cloud-sagemaker-iam-preflightEnsure a usable SageMaker execution role exists before deploying or training.SKILL.md
hf-cloud-sagemaker-production-defaultsCreate 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-selectionPick the right serving container for a SageMaker model deployment and find its current image URI.SKILL.md
hf-memHugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face HubSKILL.md
huggingface-bestUse when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores.SKILL.md
huggingface-community-evalsRun evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware.SKILL.md
huggingface-datasetsUse this skill for Hugging Face Dataset Viewer API workflows that fetch subset/split metadata, paginate rows, search text, apply filters, download parquet URLs, and read size or statistics.SKILL.md
huggingface-gradioBuild Gradio web UIs and demos in Python.SKILL.md
huggingface-llm-trainerTrain or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure.SKILL.md
huggingface-local-modelsUse to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm.SKILL.md
huggingface-lora-space-builderBuild and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA.SKILL.md
huggingface-paper-publisherPublish and manage research papers on Hugging Face Hub.SKILL.md
huggingface-papersLook up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models/datasets/spaces, Github repo and project page.SKILL.md
huggingface-spacesBuild, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging, buckets, inference providers, community grants.SKILL.md
huggingface-tool-builderUse this skill when the user wants to build tool/scripts or achieve a task where using data from the Hugging Face API would help.SKILL.md
huggingface-trackioTrack and visualize ML training experiments with Trackio.SKILL.md
huggingface-vision-trainerTrains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs.SKILL.md
huggingface-zerogpuAI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU.SKILL.md
train-sentence-transformersTrain or fine-tune sentence-transformers models across SentenceTransformer (bi-encoder; dense or static embedding model; for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), CrossEncoder (reranker; pair scoring for two-stage retrieval / pair classification), and SparseEncoder (SPLADE, sparse embedding model; for learned-sparse retrieval).SKILL.md
transformers-jsUse Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript.SKILL.md
trl-trainingTrain and fine-tune transformer language models using TRL (Transformers Reinforcement Learning).SKILL.md

Using skills in your coding agent

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

  • "Use the HF LLM trainer skill to estimate the GPU memory needed for a 70B model run."
  • "Use the HF model evaluation skill to launch run_eval_job.py on the latest checkpoint."
  • "Use the HF dataset creator skill to draft new few-shot classification templates."
  • "Use the HF paper publisher skill to index my arXiv paper and link it to my model."

Your coding agent automatically loads the corresponding SKILL.md instructions and helper scripts while it completes the task.

Contribute or customize a skill

  1. Copy one of the existing skill folders (for example, hf-datasets/) and rename it.
  2. Update the new folder's SKILL.md frontmatter:
    ---
    name: my-skill-name
    description: Describe what the skill does and when to use it
    ---
    
    # Skill Title
    Guidance + examples + guardrails
    
  3. Add or edit supporting scripts, templates, and documents referenced by your instructions.
  4. Do not add the skill to .claude-plugin/marketplace.json by default. Client marketplaces are intentionally limited to hf-cli; the full Hub CLI marketplace is generated at .claude-plugin/marketplace-internal.json.
  5. Run:
    ./scripts/publish.sh
    
    to regenerate and validate all generated metadata.
  6. Reinstall or reload the skill bundle in your coding agent so the updated folder is available.

Marketplace

The .claude-plugin/marketplace.json and .cursor-plugin/marketplace.json files intentionally expose only hf-cli for client marketplace installation. This keeps install-time manifests focused on core Hub operations and points users to hf skills add <skill-name> for the rest of the repository.

The generated .claude-plugin/marketplace-internal.json file contains the full skill list. Publish automation uploads it to the Hub bucket as marketplace.json so hf skills list, hf skills add, and hf skills update continue to see every available skill.

Newer skill-aware integrations can also pull capabilities dynamically. Hugging Face's discovery flow lets clients search skills, MCP servers, and Spaces, while Skills-over-MCP work is standardizing how skills are discovered and consumed through MCP resources.

Additional references

  • Browse the latest instructions, scripts, and templates directly at huggingface/skills.
  • Review Hugging Face documentation for the specific libraries or workflows you reference inside each skill.
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Hugging Face Skills are definitions for AI/ML tasks like dataset creation, model training, and evaluation.

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Questions about huggingface-skills.

Which coding agents can use huggingface-skills?
huggingface-skills works in any agent that reads an MCP config file: Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf. Each agent reads it from its own file; this page names the file for each one.
How do I add huggingface-skills to Claude Code?
Run these in order, one at a time. 1. In Claude Code: /plugin marketplace add huggingface/skills 2. In Claude Code: /plugin install hf-cli@huggingface-skills
Is huggingface-skills safe to install?
Low risk. Signals from public registries, not an audit.