skills/hf-mcp/SKILL.md
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
npx skillsauth add ranbot-ai/awesome-skills hf-mcpInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill when you need 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": "$HF_TOKEN"}
})
User: "What's happening with my training job?"
1. hf_jobs(operation="ps")
2. hf_jobs(operation="logs", args={"job_id": "job-xxxxx"})
User: "What models are trending right now?"
model_search(sort="trendingScore", limit=20)
User: "Tell me about Mistral-7B"
hub_repo_details(repo_ids=["mistralai/Mistral-7B-v0.1"], include_readme=true)
User: "Find GGUF versions of Llama 3"
model_search(query="Llama 3 GGUF", sort="downloads", limit=10)
User: "Transcribe this audio file"
1. space_search(query="speech to text transcription", mcp=true)
2. dynamic_space(operation="view_parameters", space_name="openai/whisper")
3. dynamic_space(operation="invoke", space_name="openai/whisper", parameters="{\"audio\": \"...\"}")
User: "Run this data sync every day at midnight"
hf_jobs(operation="scheduled uv", args={
"script": "...",
"cron": "0 0 * * *",
"flavor": "cpu-basic"
})
| Goal | Tool |
|------|------|
| Find models | model_search |
| Find datasets | dataset_search |
| Find Spaces/apps | space_search |
| Find papers | paper_search |
| Get repo README/details | hub_repo_details |
| Learn library usage | hf_doc_search → hf_doc_fetch |
| Run code on GPU/CPU | hf_jobs |
| Use Gradio apps as tools | dynamic_space |
| Generate images | gr1_flux1_schnell_infer or dynamic_space |
| Check auth | hf_whoami |
sort="trendingScore" to find what's popular nowsort="downloads" to find battle-tested optionsmcp=true in space_search to find Spaces usable as toolsinclude_readme=true in hub_repo_details for full model/dataset documentationsecrets: {"HF_TOKEN": "$HF_TOKEN"}dynamic_space(operation="discover") to see all available Space-based taskstools
Delegate coding tasks to the Grok Build CLI only when the user explicitly requests it, while the orchestrator retains review and landing responsibility.
development
--- name: graceful-shutdown description: Implement graceful shutdown for servers and workers: drain connections, finish in-flight work, release resources, and exit cleanly on SIGTERM/SIGINT. category: AI & Agents source: antigravity tags: [python, typescript, node, api, claude, ai, template, docker, kubernetes] url: https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/graceful-shutdown --- # Graceful Shutdown ## Overview A skill for implementing graceful shutdown in server
development
--- name: falsify description: The scientific thinking protocol for AI agents. Use when facing complex, ambiguous, or high-stakes questions where guessing is costly: hypothesis → attempt to break it → evidence → calibrated co category: Creative & Media source: antigravity tags: [markdown, claude, ai, agent, llm, template, design, security, rag, cro] url: https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/falsify --- # Falsify — The Scientific Thinking Protocol > Think like
tools
Configure approved delegation lanes across installed implementer CLIs, including optional model and effort choices, then write global or project config only after explicit user approval.