hf-mcp
Use Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio…
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.
$ npx -y skills add huggingface/skills --skill huggingface-datasets --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/huggingface-datasetsContext preview
The summary Claude sees to decide when to auto-load this skill.
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.
name: huggingface-datasets description: 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.
Use this skill to execute read-only Dataset Viewer API calls for dataset exploration and extraction.
1. Optionally validate dataset availability with `/is-valid`. 2. Resolve `config` + `split` with `/splits`. 3. Preview with `/first-rows`. 4. Paginate content with `/rows` using `offset` and `length` (max 100). 5. Use `/search` for text matching and `/filter` for row predicates. 6. Retrieve parquet links via `/parquet` and totals/metadata via `/size` and `/statistics`.
Pagination pattern:
curl "https://datasets-server.huggingface.co/rows?dataset=stanfordnlp/imdb&config=plain_text&split=train&offset=0&length=100" curl "https://datasets-server.huggingface.co/rows?dataset=stanfordnlp/imdb&config=plain_text&split=train&offset=100&length=100"
When pagination is partial, use response fields such as `num_rows_total`, `num_rows_per_page`, and `partial` to drive continuation logic.
Search/filter notes:
For CLI-based parquet URL discovery or SQL, use the `hf-cli` skill with `hf datasets parquet` and `hf datasets sql`.
Use one of these flows depending on dependency constraints.
Zero local dependencies (Hub UI):
curl -s "https://datasets-server.huggingface.co/parquet?dataset=<namespace>/<repo>"
Low dependency CLI flow (`npx @huggingface/hub` / `hfjs`):
export HF_TOKEN=<your_hf_token>
npx -y @huggingface/hub upload datasets/<namespace>/<repo> ./local/parquet-folder data
npx -y @huggingface/hub upload datasets/<namespace>/<repo> ./local/parquet-folder data --private
After upload, call `/parquet` to discover `<config>/<split>/<shard>` values for querying with `@~parquet`.
The Hub supports raw agent session traces from Claude Code, Codex, and Pi Agent. Upload them to Hugging Face Datasets as original JSONL files and the Hub can auto-detect the trace format, tag the dataset as `Traces`, and enable the trace viewer for browsing sessions, turns, tool calls, and model responses. Common local session directories:
Default to private dataset repos because traces can contain prompts, file paths, tool outputs, secrets, or PII. Preserve the raw `.jsonl` files and nest them by project/cwd instead of uploading every session at the dataset root.
hf repos create <namespace>/<repo> --type dataset --private --exist-ok hf upload <namespace>/<repo> ~/.codex/sessions codex/<project-or-cwd> --type dataset
Hugging Face Skills are definitions for AI/ML tasks like dataset creation, model training, and evaluation.
Repo: huggingface/skills
Use Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio…
Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub.…
Discover the user's local AWS context (active profile, region, account ID, caller identity) at the start of any AWS task. Use this skill before any other AWS…
Set up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3. Use this skill whenever Python code will be…
Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on…
Ensure a usable SageMaker execution role exists before deploying or training. Use this skill whenever about to create a SageMaker endpoint, model, training…