Query HuggingFace Hub, OpenML, GitHub, and papers for ranked ML datasets.
npx skills add https://github.com/unstun/dqn10 --skill dataset-discovery-unstun
Please help me install this Agent Skill. Skill: dataset-discovery Source: https://github.com/unstun/dqn10/tree/main/.claude/skills/dataset-discovery Command: npx skills add https://github.com/unstun/dqn10 --skill dataset-discovery-unstun
Multi-source ML dataset discovery accelerates research by surfacing relevant datasets from multiple sources where you work.
Search for datasets matching your task and review the ranked results.
High-intent search queries and answers about installing and using this skill.
To find relevant ML datasets, query multiple sources like HuggingFace Hub, OpenML, GitHub, and papers to receive a deduplicated, ranked list. Results include metadata such as modality, license, size, and source to inform your selection.
Multi-source dataset discovery works by querying public APIs from HuggingFace Hub, OpenML, GitHub, and papers, then returning a deduplicated and ranked list. It gracefully handles API failures with sensible fallbacks to ensure continuous search capability.
You can search for image classification, NLP benchmark, or tabular data datasets by applying your task query to multiple sources. The search aggregates results and provides modality, license, and size metadata for informed selection across these tasks.
You need internet access to query public APIs from HuggingFace Hub, OpenML, GitHub, and papers for dataset discovery. The process supports optional local workspace outputs but requires connectivity to retrieve the multi-source results.
The best way to deduplicate and rank datasets is by querying multiple sources simultaneously, which aggregates results from HuggingFace, OpenML, GitHub, and papers into a single ranked list. This provides modality, license, and size metadata for comparison.
API failures during dataset search are handled gracefully with sensible fallbacks to ensure the process continues. This allows the multi-source query to return available results from HuggingFace, OpenML, GitHub, or papers even if one source fails.
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