hf-mcp
Use Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run…
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.
Claude Code
$ npx -y skills add huggingface/skills --skill huggingface-datasets --agent claude-code
Codex
$ npx skills add huggingface/skills --skill huggingface-datasets -a codex -y
Cursor
$ npx skills add huggingface/skills --skill huggingface-datasets -a cursor -y
Gemini CLI
$ npx skills add huggingface/skills --skill huggingface-datasets -a gemini-cli -y
GitHub Copilot
$ npx skills add huggingface/skills --skill huggingface-datasets -a github-copilot -y
OpenCode
$ npx skills add huggingface/skills --skill huggingface-datasets -a opencode -y
Windsurf
$ npx skills add huggingface/skills --skill huggingface-datasets -a windsurf -y
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
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