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 file with YAML frontmatter (name and description) followed by the guidance your coding agent follows while the skill is active. > [!NOTE] > 'Skills' is actually an Anthropic term used within Claude AI and Claude Code and not adopted by other agent tools, but we love it! OpenAI Codex uses the open Agent Skills format, where each skill is a directory with a file that Codex discovers from standard locations documented in the Codex Skills guide. Codex can also work with an file. Google Gemini uses 'extensions' to define the instructions for your coding agent in a file. **This repo is compatible with all of them, and more!** > [!TIP] > If your agent doesn't support skills, you can use directly as a fallback. Installation Hugging Face skills are compatible with Claude Code, Codex, Gemini CLI, and Cursor. Claude Code • Register the repository as a plugin marketplace: • To install a skill, run: For example: Codex • Copy or symlink any skills you want to use from this repository's directory into one of Codex's standard locations (for example, or ) as described in the Codex Skills guide. • Once a skill is available in one of those locations, Codex will discover it using the Agent Skills standard and load the instructions when it decides to use that skill or when you explicitly invoke it. • If your Codex setup still relies on , you can use the generated file in this repo as a fallback bundle of instructions. Gemini CLI • This repo includes to integrate with the Gemini CLI. • Install locally: or use the GitHub URL: • See Gemini CLI extensions docs for more help. Cursor This repository includes Cursor plugin manifests: • • (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: 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 | |------|-------------|---------------| | | Build Gradio web UIs and demos in Python. Use when creating or editing Gradio apps, components, event listeners, layouts, or chatbots. | SKILL.md | | | Execute Hugging Face Hub operations using the hf CLI. Download models/datasets, upload files, manage repos, and run cloud compute jobs. | SKILL.md | | | 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 | | | Create and manage datasets on Hugging Face Hub. Supports initializing repos, defining configs/system prompts, streaming row updates, and SQL-based dataset querying/transformation. | SKILL.md | | | 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 | | | Run compute jobs on Hugging Face infrastructure. Execute Python scripts, manage scheduled jobs, and monitor job status. | SKILL.md | | | 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 | | | 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 | | | Build reusable scripts for Hugging Face API operations. Useful for chaining API calls or automating repeated tasks. | SKILL.md | | | Track and visualize ML training experiments with Trackio. Log metrics via Python API and retrieve them via CLI. Supports real-time dashboards synced to HF Spaces. | SKILL.md | | | Train and fine-tune object detection models (RTDETRv2, YOLOS, DETR and others) and image classification models (timm and transformers models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3) using Transformers Trainer API on Hugging Face Jobs infrastructure or locally. Includes COCO dataset format support, Albumentations augmentation, mAP/mAR metrics, trackio tracking, hardware selection, and Hub persistence. | SKILL.md | | | Run state-of-the-art machine learning models directly in JavaScript/TypeScript for NLP, computer vision, audio processing, and multimodal tasks. Works in Node.js and browsers with WebGPU/WASM using Hugging Face models. | 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 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 instructions and helper scripts while it completes the task. Contribute or customize a skill • Copy one of the existing skill folders (for example, ) and rename it. • Update the new folder's frontmatter: • Add or edit supporting scripts, templates, and doc…