“datasets”

Use this skill when working with Hugging Face Datasets: loading local or Hub datasets, defining Features schemas, processing/streaming datasets, converting…

Load, stream, process, and publish datasets with the HuggingFace datasets library.

Use when ingesting, validating, curating, or querying production sensor data as a versioned dataset-of-record, or wiring the dataset-ingest-curate workflow.

Download genomes, genes, virus sequences, and taxonomy data from NCBI using the datasets and dataformat CLI tools.

Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script).

Open a Zira Vision or Pallet Vision dataset for annotation, or its versions, models, training or validation, in Zira with the context filled in.

Explore, validate, filter, paginate, export, and prepare Hugging Face datasets through the Dataset Viewer API and Hub tooling.

Use this skill for Hugging Face Dataset Viewer API workflows that fetch subset/split metadata, paginate rows, search text, apply filters, download parquet…

Use when you must prove an analysis ran on the intended data or lock a dataset version.

Split datasets into training, validation, and test partitions with the right stratification and temporal rules.

Answer questions from a named startup or community dataset using retrieved evidence.

Convert workflow HDF5 recordings to LeRobot datasets for training or browser inspection.

Apply hugging face datasets in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation.

Find and rank datasets for a replication or ML task by requirements, license, and availability.

Validates dataset formatting and quality for SageMaker model fine-tuning (SFT, DPO, or RLVR).

Generates code that transforms datasets between ML schemas for model training or evaluation.

Helps users of the Timefold Employee Shift Scheduling (ESS) model understand an optimized schedule by fetching and interpreting the dataset via the API.

Version datasets with checksums, manifests, and semantic versioning.

Use this skill when designing, cleaning, deduplicating, or documenting datasets for model training and evaluation including schema design, class imbalance…

Helps users of the Timefold Field Service Routing (FSR) model understand an optimized route plan by fetching and interpreting the dataset via the API.

Helps users of the Timefold Pick-up and Delivery Routing (PDR) model understand an optimized route plan by fetching and interpreting the dataset via the API.

Clean, normalize, and prepare raw datasets for analysis or ML.

Discover startup founders and employees from the full parsed dataset, company website, web search and LinkedIn.

Curate generated images into a clean training dataset, validate metadata, remove duplicates, and sync manifests/assets to Hugging Face.