hf-cli
Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and…
Use Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio Spaces as AI tools. Available when connected to the HF MCP server.
Claude Code
$ npx -y skills add huggingface/skills --skill hf-mcp --agent claude-code
Codex
$ npx skills add huggingface/skills --skill hf-mcp -a codex -y
Cursor
$ npx skills add huggingface/skills --skill hf-mcp -a cursor -y
Gemini CLI
$ npx skills add huggingface/skills --skill hf-mcp -a gemini-cli -y
GitHub Copilot
$ npx skills add huggingface/skills --skill hf-mcp -a github-copilot -y
OpenCode
$ npx skills add huggingface/skills --skill hf-mcp -a opencode -y
Windsurf
$ npx skills add huggingface/skills --skill hf-mcp -a windsurf -y
name: hf-mcp description: Use Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio Spaces as AI tools. Available when connected to the HF MCP server.
Connect AI assistants to the Hugging Face Hub. Setup: https://huggingface.co/settings/mcp
User: "Find the best model for code generation" 1. model_search(task="text-generation", query="code", sort="trendingScore", limit=10) 2. hub_repo_details(repo_ids=["top-result-id"], include_readme=true)
User: "Compare Llama vs Qwen for text generation" 1. model_search(author="meta-llama", task="text-generation", sort="downloads", limit=5) 2. model_search(author="Qwen", task="text-generation", sort="downloads", limit=5) 3. hub_repo_details(repo_ids=["meta-llama/Llama-3.2-1B", "Qwen/Qwen3-8B"], include_readme=true)
User: "Find datasets for sentiment analysis in English" 1. dataset_search(query="sentiment", tags=["language:en", "task_categories:text-classification"], sort="downloads") 2. hub_repo_details(repo_ids=["top-dataset-id"], repo_type="dataset", include_readme=true)
User: "Find a tool that can remove image backgrounds"
1. space_search(query="background removal", mcp=true)
2. dynamic_space(operation="view_parameters", space_name="result-space-id")
3. dynamic_space(operation="invoke", space_name="result-space-id", parameters="{...}")User: "Create an image of a robot reading a book" 1. dynamic_space(operation="discover") # See available tasks 2. gr1_flux1_schnell_infer(prompt="a robot sitting in a library reading a book, warm lighting, detailed")
User: "What are the latest papers on RLHF?" 1. paper_search(query="reinforcement learning from human feedback", results_limit=10) 2. hub_repo_details(repo_ids=["paper-linked-model"], include_readme=true) # If paper links to models
User: "How do I fine-tune with LoRA using PEFT?" 1. hf_doc_search(query="LoRA fine-tuning", product="peft") 2. hf_doc_fetch(doc_url="https://huggingface.co/docs/peft/...")
User: "Run this Python script on a GPU"
hf_jobs(operation="uv", args={
"script": "# /// script\n# dependencies = [\"torch\"]\n# ///\nimport torch\nprint(torch.cuda.is_available())",
"flavor": "t4-small"
})User: "Run my training script on an A10G"
hf_jobs(operation="run", args={
"image": "pytorch/pytorch:2.5.1-cuda12.4-cudnn9-runtime",
"command": ["/bin/sh", "-lc", "pip install transformers trl && python train.py"],
"flavor": "a10g-small",
"secrets": {"HF_TOKEN": "$HF_TOKEN"}
})User: "What's happening with my training job?"
1. hf_jobs(operation="ps")
2. hf_jobs(operation="logs", args={"job_id": "job-xxxxx"})User: "What models are trending right now?" model_search(sort="trendingScore", limit=20)
User: "Tell me about Mistral-7B" hub_repo_details(repo_ids=["mistralai/Mistral-7B-v0.1"], include_readme=true)
User: "Find GGUF versions of Llama 3" model_search(query="Llama 3 GGUF", sort="downloads", limit=10)
User: "Transcribe this audio file"
1. space_search(query="speech to text transcription", mcp=true)
2. dynamic_space(operation="view_parameters", space_name="openai/whisper")
3. dynamic_space(operation="invoke", space_name="openai/whisper", parameters="{\"audio\": \"...\"}")User: "Run this data sync every day at midnight"
hf_jobs(operation="scheduled uv", args={
"script": "...",
"cron": "0 0 * * *",
"flavor": "cpu-basic"
})| Goal | Tool | |------|------| | Find models | `model_search` | | Find datasets | `dataset_search` | | Find Spaces/apps | `space_search` | | Find papers | `paper_search` | | Get repo README/details | `hub_repo_details` | | Learn library usage | `hf_doc_search` → `hf_doc_fetch` | | Run code on GPU/CPU | `hf_jobs` | | Use Gradio apps as tools | `dynamic_space` | | Generate images | `gr1_flux1_schnell_infer` or `dynamic_space` | | Check auth | `hf_whoami` |
Hugging Face Skills are definitions for AI/ML tasks like dataset creation, model training, and evaluation.
Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and…
Discover the user's local AWS context (active profile, region, account ID, caller identity) at the start of any AWS task. Use…
Set up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3. Use this skill…
Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host,…
Ensure a usable SageMaker execution role exists before deploying or training. Use this skill whenever about to create a SageMaker…
Create a SageMaker endpoint (real-time, real-time scale-to-zero, or async) with autoscaling, CloudWatch alarms, and tagging…
Internals of the Feast codebase — how each component works, where the key abstractions live, and the data flow through the…
Query and browse evaluation results stored in MLflow. Use when the user wants to look up runs by invocation ID, compare metrics…