By huggingface
Manage the full ML lifecycle on Hugging Face Hub: search and select models, train or fine-tune with TRL/Unsloth, evaluate locally, build and deploy Gradio demos on Spaces, publish research papers, and monitor training metrics — all from the command line or agent.
Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or scheduling jobs on Hugging Face infrastructure; managing Hugging Face repos; discussions and pull requests; browsing models, datasets and spaces; reading, searching, or browsing academic papers; managing collections; querying datasets; configuring spaces; setting up webhooks; or deploying and managing HF Inference Endpoints. Make sure to use this skill whenever the user mentions 'hf', 'huggingface', 'Hugging Face', 'huggingface-cli', or 'hugging face cli', or wants to do anything related to the Hugging Face ecosystem and to AI and ML in general. Also use for cloud storage needs like training checkpoints, data pipelines, or agent traces. Use even if the user doesn't explicitly ask for a CLI command. Replaces the deprecated `huggingface-cli`.
Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub
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. Triggers on: "best model for X", "what model should I use for", "top models for [task]", "which model runs on my laptop/machine/device", "recommend a model for", "what LLM should I use for", "compare models for", "what's state of the art for", or any question about choosing an AI model for a specific use case. Always use this skill when the user wants model recommendations or comparisons, even if they don't explicitly mention HuggingFace or benchmarks.
Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.
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.
External network access
Connects to servers outside your machine
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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.
[!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 everyhfcommand (search models, manage datasets and buckets, launch Spaces, run jobs) and is generated from your locally installed CLI so it stays current.
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.mddirectly as a fallback.
The skills in this repository are also available through:
Hugging Face skills are compatible with Claude Code, Codex, Gemini CLI, and Cursor.
/plugin marketplace add huggingface/skills
/plugin install <skill-name>@huggingface/skills
For example:
/plugin install hf-cli@huggingface/skills
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.
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.
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.
This repo includes gemini-extension.json to integrate with the Gemini CLI.
Install locally:
gemini extensions install . --consent
or use the GitHub URL:
gemini extensions install https://github.com/huggingface/skills.git --consent
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.
For contributors, regenerate manifests with:
./scripts/publish.sh
This repository contains a few skills to get you started. You can also contribute your own skills to the repository.
npx claudepluginhub huggingface/skills --plugin trl-trainingRun ml-intern tasks from Claude Code.
ML engineering plugin: Give your AI coding agent ML engineering superpowers.
ML/perf investigation skills: topic, plan, judge, run, sweep
Evaluate and compare ML model performance metrics
Transfer learning adaptation
Write SQL, explore datasets, and generate insights faster. Build visualizations and dashboards, and turn raw data into clear stories for stakeholders.
Give your AI a memory — mine projects and conversations into a searchable palace. 33 MCP tools, auto-save hooks, and guided setup.