Find and rank datasets from HuggingFace, OpenML, GitHub, and papers.
npx skills add https://github.com/OpenLAIR/dr-claw --skill dataset-discovery
Please help me install this Agent Skill. Skill: dataset-discovery Source: https://github.com/OpenLAIR/dr-claw/tree/main/skills/dataset-discovery Command: npx skills add https://github.com/OpenLAIR/dr-claw --skill dataset-discovery
Multi-source dataset discovery and ranking for ML tasks, saving researchers time locating relevant data.
Find and rank datasets for a given ML task across multiple sources.
High-intent search queries and answers about installing and using this skill.
To find relevant ML datasets, you can search across HuggingFace, OpenML, GitHub, and papers simultaneously. This aggregates results into a deduplicated, ranked list with metadata and source provenance tailored to your specific research needs.
Yes, you can filter dataset search results by modality, size, and license. This allows you to tailor the discovered datasets to your specific constraints and ensures the output metadata matches your project requirements.
The best way to deduplicate dataset search results is using an automated discovery tool that aggregates sources like HuggingFace and GitHub. It removes duplicates and applies relevance ranking to surface high-quality datasets.
You can preview metadata and pull representative samples for datasets found on GitHub and papers. This quick evaluation feature allows you to inspect the data before committing to downstream use.
Yes, you need the requests library installed in your environment. It is a required dependency for executing the scripts that query multiple sources and retrieve dataset metadata.
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