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Get Started Free →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/skills/sickn33-hf-mcp/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-06 | ✗→✓ | ▲ Improved | 33% | 0% |
| case-01 | ✗→✓ | ▲ Improved | -22% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 18% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 37% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 205% | 0% |
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 modelsUser: "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 tasks| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-06 | fail→pass | 10,771 | 3,963 | -63% | 1 | 1 | 0% | 1,752 | 2,335 | +33% | 0 | 0 | — |
case-01 | fail→pass | 19,275 | 6,203 | -68% | 1 | 1 | 0% | 3,598 | 2,798 | -22% | 0 | 0 | — |
case-02 | fail→fail | 17,704 | 5,376 | -70% | 1 | 1 | 0% | 3,046 | 1,900 | -38% | 0 | 0 | — |
case-03 | fail→pass | 12,598 | 2,729 | -78% | 1 | 1 | 0% | 1,772 | 2,090 | +18% | 0 | 0 | — |
case-04 | fail→pass | 8,030 | 2,882 | -64% | 1 | 1 | 0% | 1,521 | 2,083 | +37% | 0 | 0 | — |
case-05 | fail→pass | 5,629 | 3,408 | -39% | 1 | 1 | 0% | 724 | 2,206 | +205% | 0 | 0 | — |
case-07 | fail→pass | 5,419 | 4,204 | -22% | 1 | 1 | 0% | 789 | 2,281 | +189% | 0 | 0 | — |
case-08 | fail→pass | 8,764 | 4,459 | -49% | 1 | 1 | 0% | 1,338 | 2,378 | +78% | 0 | 0 | — |
case-09 | fail→pass | 14,157 | 2,988 | -79% | 1 | 1 | 0% | 2,140 | 2,140 | 0% | 0 | 0 | — |
case-10 | fail→pass | 8,961 | 2,840 | -68% | 1 | 1 | 0% | 1,790 | 2,108 | +18% | 0 | 0 | — |
case-11 | pass→pass | 2,279 | 1,987 | -13% | 1 | 1 | 0% | 253 | 1,805 | +613% | 0 | 0 | — |
case-12 | pass→pass | 8,769 | 3,172 | -64% | 1 | 1 | 0% | 1,465 | 2,089 | +43% | 0 | 0 | — |
case-13 | fail→pass | 10,445 | 3,012 | -71% | 1 | 1 | 0% | 1,908 | 2,158 | +13% | 0 | 0 | — |
case-14 | fail→pass | 7,554 | 3,878 | -49% | 1 | 1 | 0% | 1,238 | 2,231 | +80% | 0 | 0 | — |
case-15 | fail→pass | 12,221 | 2,301 | -81% | 1 | 1 | 0% | 2,020 | 1,945 | -4% | 0 | 0 | — |
case-16 | pass→pass | 2,117 | 2,182 | +3% | 1 | 1 | 0% | 259 | 1,943 | +650% | 0 | 0 | — |
case-17 | fail→pass | 4,288 | 2,544 | -41% | 1 | 1 | 0% | 645 | 2,030 | +215% | 0 | 0 | — |
case-18 | fail→pass | 7,440 | 2,247 | -70% | 1 | 1 | 0% | 1,239 | 1,980 | +60% | 0 | 0 | — |
case-19 | pass→pass | 8,975 | 7,205 | -20% | 1 | 1 | 0% | 1,500 | 2,928 | +95% | 0 | 0 | — |
case-20 | pass→pass | 9,591 | 3,989 | -58% | 1 | 1 | 0% | 1,792 | 2,304 | +29% | 0 | 0 | — |
case-21 | pass→fail | 11,888 | 5,641 | -53% | 1 | 1 | 0% | 2,035 | 1,782 | -12% | 0 | 0 | — |
case-22 | pass→pass | 11,391 | 9,688 | -15% | 1 | 1 | 0% | 2,015 | 3,299 | +64% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 20 counted toward the lift figure. The other 2 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +59 percentage points is the difference between those two pass rates over the 20 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.