huggingface-datasets

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.

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Repository: huggingface/skills
Last source activity: April 30, 2026 at 13:17
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hf-cli

Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or scheduling jobs on Hugging Face infrastructure; managing Hugging Face repos; discussions and pull requests; browsing models, datasets and spaces; reading, searching, or browsing academic papers; managing collections; querying datasets; configuring spaces; setting up webhooks; or deploying and managing HF Inference Endpoints. Make sure to use this skill whenever the user mentions 'hf', 'huggingface', 'Hugging Face', 'huggingface-cli', or 'hugging face cli', or wants to do anything related to the Hugging Face ecosystem and to AI and ML in general. Also use for cloud storage needs like training checkpoints, data pipelines, or agent traces. Use even if the user doesn't explicitly ask for a CLI command. Replaces the deprecated `huggingface-cli`.

2026-08-27

train-sentence-transformers

Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker, pair scoring for two-stage retrieval / pair classification), `SparseEncoder` (SPLADE, sparse embedding model for learned-sparse retrieval), and `MultiVectorEncoder` (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.

2026-08-18

hf-cloud-sagemaker-deployment-planner

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 SageMaker or AWS — including phrases like "deploy a model", "host this LLM on AWS", "serve this embedding model", "deploy a reranker", "deploy a text-to-image / diffusion model", "host this for async inference", "create an endpoint", "serve my fine-tuned model", or any request that involves making a model available for inference on AWS. Use this even when the user is vague (e.g. "I just want to get this running on AWS, you figure it out"). Works for text-generation LLMs, embedding models, rerankers, classifiers, text-to-image / diffusion models — picks the right serving stack and chooses between real-time and async inference. This is the entry-point skill for SageMaker deployment work — it asks clarifying questions, picks a deployment pathway, and coordinates the other deployment skills.

2026-08-03

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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.

Hugging Face Dataset Viewer

Use this skill to execute read-only Dataset Viewer API calls for dataset exploration and extraction.

Core workflow

Optionally validate dataset availability with /is-valid.
Resolve config + split with /splits.
Preview with /first-rows.
Paginate content with /rows using offset and length (max 100).
Use /search for text matching and /filter for row predicates.
Retrieve parquet links via /parquet and totals/metadata via /size and /statistics.

Defaults

Base URL: https://datasets-server.huggingface.co
Default API method: GET
Query params should be URL-encoded.
offset is 0-based.
length max is usually 100 for row-like endpoints.
Gated/private datasets require Authorization: Bearer <HF_TOKEN>.

Dataset Viewer

Validate dataset: /is-valid?dataset=<namespace/repo>
List subsets and splits: /splits?dataset=<namespace/repo>
Preview first rows: /first-rows?dataset=<namespace/repo>&config=<config>&split=<split>
Paginate rows: /rows?dataset=<namespace/repo>&config=<config>&split=<split>&offset=<int>&length=<int>
Search text: /search?dataset=<namespace/repo>&config=<config>&split=<split>&query=<text>&offset=<int>&length=<int>
Filter with predicates: /filter?dataset=<namespace/repo>&config=<config>&split=<split>&where=<predicate>&orderby=<sort>&offset=<int>&length=<int>
List parquet shards: /parquet?dataset=<namespace/repo>
Get size totals: /size?dataset=<namespace/repo>
Get column statistics: /statistics?dataset=<namespace/repo>&config=<config>&split=<split>
Get Croissant metadata (if available): /croissant?dataset=<namespace/repo>

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:

/search matches string columns (full-text style behavior is internal to the API).
/filter requires predicate syntax in where and optional sort in orderby.
Keep filtering and searches read-only and side-effect free.

For CLI-based parquet URL discovery or SQL, use the hf-cli skill with hf datasets parquet and hf datasets sql.

Creating and Uploading Datasets

Use one of these flows depending on dependency constraints.

Zero local dependencies (Hub UI):

Create dataset repo in browser: https://huggingface.co/new-dataset
Upload parquet files in the repo "Files and versions" page.
Verify shards appear in Dataset Viewer:

curl -s "https://datasets-server.huggingface.co/parquet?dataset=<namespace>/<repo>"

Low dependency CLI flow (npx @huggingface/hub / hfjs):

Set auth token:

export HF_TOKEN=<your_hf_token>

Upload parquet folder to a dataset repo (auto-creates repo if missing):

npx -y @huggingface/hub upload datasets/<namespace>/<repo> ./local/parquet-folder data

Upload as private repo on creation:

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.

Agent Traces

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:

Claude Code: ~/.claude/projects
Codex: ~/.codex/sessions
Pi: ~/.pi/agent/sessions

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

huggingface-datasets

Source facts

Install options

Review the source files

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