## File: README.md

# 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](https://agentskills.io/home) format.

> [!NOTE]
> **Just want to give your agent access to the Hugging Face Hub?** Start with [`hf-cli`](https://huggingface.co/docs/hub/agents-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`](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:  
   
```
/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

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](https://developers.openai.com/codex/skills/).

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`](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
```

4. See [Gemini CLI extensions docs](https://geminicli.com/docs/extensions/#installing-an-extension) 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:

```bash
./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` | 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](skills/hf-cli/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](skills/hf-cloud-aws-context-discovery/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](skills/hf-cloud-python-env-setup/SKILL.md) |
| `hf-cloud-sagemaker-deployment-planner` | Plan and coordinate the deployment of a model to Amazon SageMaker AI. | [SKILL.md](skills/hf-cloud-sagemaker-deployment-planner/SKILL.md) |
| `hf-cloud-sagemaker-iam-preflight` | Ensure a usable SageMaker execution role exists before deploying or training. | [SKILL.md](skills/hf-cloud-sagemaker-iam-preflight/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](skills/hf-cloud-sagemaker-production-defaults/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](skills/hf-cloud-serving-image-selection/SKILL.md) |
| `hf-mem` | Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub | [SKILL.md](skills/hf-mem/SKILL.md) |
| `huggingface-best` | Use 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](skills/huggingface-best/SKILL.md) |
| `huggingface-community-evals` | Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. | [SKILL.md](skills/huggingface-community-evals/SKILL.md) |
| `huggingface-datasets` | Use 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](skills/huggingface-datasets/SKILL.md) |
| `huggingface-gradio` | Build Gradio web UIs and demos in Python. | [SKILL.md](skills/huggingface-gradio/SKILL.md) |
| `huggingface-llm-trainer` | Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. | [SKILL.md](skills/huggingface-llm-trainer/SKILL.md) |
| `huggingface-local-models` | Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. | [SKILL.md](skills/huggingface-local-models/SKILL.md) |
| `huggingface-lora-space-builder` | Build and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA. | [SKILL.md](skills/huggingface-lora-space-builder/SKILL.md) |
| `huggingface-paper-publisher` | Publish and manage research papers on Hugging Face Hub. | [SKILL.md](skills/huggingface-paper-publisher/SKILL.md) |
| `huggingface-papers` | Look 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](skills/huggingface-papers/SKILL.md) |
| `huggingface-spaces` | Build, 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](skills/huggingface-spaces/SKILL.md) |
| `huggingface-tool-builder` | Use 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](skills/huggingface-tool-builder/SKILL.md) |
| `huggingface-trackio` | Track and visualize ML training experiments with Trackio. | [SKILL.md](skills/huggingface-trackio/SKILL.md) |
| `huggingface-vision-trainer` | Trains 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](skills/huggingface-vision-trainer/SKILL.md) |
| `huggingface-zerogpu` | AI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU. | [SKILL.md](skills/huggingface-zerogpu/SKILL.md) |
| `train-sentence-transformers` | Train 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](skills/train-sentence-transformers/SKILL.md) |
| `transformers-js` | Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. | [SKILL.md](skills/transformers-js/SKILL.md) |
| `trl-training` | Train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning). | [SKILL.md](skills/trl-training/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:
   ```markdown
   ---
   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
   ./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](https://github.com/huggingface/skills).
- Review Hugging Face documentation for the specific libraries or workflows you reference inside each skill.

---

## File: apps/quests/README.md

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

# 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

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:

| Date | Event | Link |
|------|-------|------|
| Dec 2 (Mon) | Week 1 Quest Released | [Evaluate a Hub Model](02_evaluate-hub-model.md) |
| Dec 4 (Wed) | Livestream 1 | [Q&A 1](https://youtube.com/live/rworGSh-Rgk?feature=share) |
| Dec 9 (Mon) | Week 2 Quest Released | [Publish a Hub Dataset](03_publish-hub-dataset.md) |
| Dec 11 (Wed) | Livestream 2 | TBA |
| Dec 16 (Mon) | Week 3 Quest Released | [Supervised Fine-Tuning](04_sft-finetune-hub.md) |
| Dec 18 (Wed) | Livestream 3 | TBA |
| Dec 23 (Mon) | Week 4 Community Sprint | TBA |
| Dec 31 (Tue) | Hackathon Ends | TBA

## Getting Started

### 1. Join the Organization

Join [hf-skills](https://huggingface.co/organizations/hf-skills/share/KrqrmBxkETjvevFbfkXeezcyMbgMjjMaOp) on Hugging Face. This is where your contributions will be tracked and updated on the leaderboard.

### 2. Set Up Your Coding Agent

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](../README.md).

### 3. Get Your HF Token

Most quests require a Hugging Face token with write access:

```bash
# 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

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

Point your coding agent at the relevant configuration. Check the [README](../README.md) for instructions on how to use the skills with your coding agent.

## Your First Quest

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



[Leaderboard](https://hf-skills-hacker-leaderboard.hf.space)

## Earning XP

Each quest has three tiers:

| Tier | What it takes | XP |
|------|---------------|-----|
| 🐢 | Complete the basics | 50-75 XP |
| 🐕 | Go deeper with more features | 100-125 XP |
| 🦁 | Ship something impressive | 200-225 XP |

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

## Getting Help

- [Discord](https://discord.com/channels/879548962464493619/1442881667986624554) — Join the Hugging Face Discord for real-time help
- [Livestreams](https://www.youtube.com/@HuggingFace/streams) — Weekly streams with walkthroughs and Q&A
- [Issues](https://github.com/huggingface/skills/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

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

# Hackers Leaderboard

Tracks engagement from the [hf-skills](https://huggingface.co/hf-skills) organization for the hackathon leaderboard.

## How Points Work

Simple and fair - **1 point per activity**:

| Activity | Points |
|----------|--------|
| 💬 Open a discussion | 1 |
| 📝 Post a comment | 1 |
| 🔀 Open a PR | 1 |
| 📦 Own/create a repo | 1 |

## Scripts

### Collect Points

```bash
# 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

| Flag | Description |
|------|-------------|
| `--scan-external` | Scan trending repos across Hub for member activity |
| `--repo-type` | Filter external scan to: `models`, `datasets`, `spaces` |
| `--push-to-hub` | Push results to HF dataset |
| `--repo-id` | Target dataset repo (default: `hf-skills/hackers-leaderboard`) |
| `--output` | Local JSON output path |

### Run the App

```bash
HF_TOKEN=$HF_TOKEN python app.py
```

## API

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

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