skills Documentation

File: README.md Copy Chapter

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? Copy Chapter

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 Copy Chapter

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

Claude Code Copy Section

1. Register the repository as a plugin marketplace:

/plugin marketplace add huggingface/skills

2. Install the CLI skill:

/plugin install hf-cli@huggingface/skills

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

hf skills add <skill-name>

Codex Copy Section

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 Copy Section

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 Copy Section

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 Copy Chapter

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

Available skills Copy Section

Using skills in your coding agent Copy Section

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 Copy Section

1. Copy one of the existing skill folders (for example, hf-datasets/) and rename it.
2. Update the new folder's SKILL.md frontmatter:
   markdown Copy

   ---
   name: my-skill-name
   ---
   
   # 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:
   bash Copy

   ./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 Copy Section

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 Copy Section

• 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.

File: apps/quests/README.md Copy Chapter

title: README emoji: 🐠 colorFrom: yellow colorTo: gray sdk: static pinned: false Copy Chapter

Humanity's Last Hackathon (of 2025)

Welcome to our hackathon!

Whether you’re a tooled up ML engineer, a classicist NLP dev, or an AGI pilled vibe coder, this hackathon is going to be hard work! We’re going to take the latest and greatest coding agents
and use them to level up open source AI. After all, why use December to relax and spend time with loved ones, when you can solve AI for all humanity? Jokes aside, this hackathon is not
about learning skills from zero or breaking things down in their simplest components. It’s about collaborating, shipping, and making a difference for the open source community.

What We're Building Copy Chapter

Over four weeks, we're using coding agents to level up the open source AI ecosystem:

• Week 1 — Evaluate models and build a distributed leaderboard
• Week 2 — Create high-quality datasets for the community
• Week 3 — Fine-tune and share models on the Hub
• Week 4 — Sprint to the finish line together

Every contribution earns XP. Top contributors make the leaderboard. Winners get prizes!

Here's the schedule:

Getting Started Copy Chapter

1. Join the Organization Copy Section

Join hf-skills on Hugging Face. This is where your contributions will be tracked and updated on the leaderboard.

2. Set Up Your Coding Agent Copy Section

Use whatever coding agent you prefer:

• Claude Code — claude in your terminal
• Codex — codex CLI
• Gemini CLI — gemini in your terminal
• Cursor / Windsurf — IDE-based agents
• Open source — aider, continue, etc.

The skills in this repo work with any agent that can read markdown instructions and run Python scripts. To install the skills, follow the instructions in the README.

3. Get Your HF Token Copy Section

Most quests require a Hugging Face token with write access:

# mac/linux
curl -LsSf https://hf.co/cli/install.sh | bash

# windows
powershell -ExecutionPolicy ByPass -c "irm https://hf.co/cli/install.ps1 | iex"

# Login (creates/stores your token)
hf auth login

This will set your HF_TOKEN environment variable.

4. Clone the Skills Repo Copy Section

git clone https://github.com/huggingface/skills.git
cd skills

Point your coding agent at the relevant configuration. Check the README for instructions on how to use the skills with your coding agent.

Your First Quest Copy Chapter

Week 1 is live! Head to 02_evaluate-hub-model.md to start evaluating models and climb the leaderboard.

Leaderboard

Earning XP Copy Chapter

Each quest has three tiers:

You can complete multiple tiers, and you can complete the same quest multiple times with different models/datasets/spaces.

Getting Help Copy Chapter

• Discord — Join the Hugging Face Discord for real-time help
• Livestreams — Weekly streams with walkthroughs and Q&A
• Issues — Open an issue in this repo if you're stuck

To join the Hackathon, join the organization on the hub and setup your coding agent.

Ready? Let's ship some AI. 🚀

File: apps/hackers-leaderboard/README.md Copy Chapter

title: Hackers Leaderboard emoji: 🏆 colorFrom: purple colorTo: pink sdk: gradio sdk_version: 5.50.0 app_file: app.py pinned: false Copy Chapter

Hackers Leaderboard

Tracks engagement from the hf-skills organization for the hackathon leaderboard.

How Points Work Copy Chapter

Simple and fair - 1 point per activity:

Scripts Copy Chapter

Collect Points Copy Section

# Collect org activity only
HF_TOKEN=$HF_TOKEN python collect_points.py

# Also scan trending repos for member PRs/discussions
HF_TOKEN=$HF_TOKEN python collect_points.py --scan-external

# Scan only specific repo types
HF_TOKEN=$HF_TOKEN python collect_points.py --scan-external --repo-type models
HF_TOKEN=$HF_TOKEN python collect_points.py --scan-external --repo-type models datasets

# Push to HF dataset
HF_TOKEN=$HF_TOKEN python collect_points.py --scan-external --push-to-hub

# Custom output
python collect_points.py --output my_leaderboard.json --repo-id my-org/my-dataset

Options Copy Section

Run the App Copy Section

HF_TOKEN=$HF_TOKEN python app.py

API Copy Chapter

The collector scans:

• All models, datasets, and spaces in the org
• All discussions and PRs on those repos
• All comments on discussions

Results are saved as JSONL for easy dataset consumption.

Output Format Copy Chapter

{
  "username": "user123",
  "total_points": 15,
  "discussions_opened": 3,
  "comments_made": 8,
  "prs_opened": 2,
  "repos_owned": 2
}