skills

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 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: or use the GitHub URL: 4. 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 <!-- This table is auto-generated by scripts/generate_agents.py. Do not edit manually. --> <!-- BEGIN_SKILLS_TABLE --> | 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