Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
$npx -y skills add k-dense-ai/scientific-agent-skills --skill daskInstalls into the current project.
Run `npx skills use "https://github.com/k-dense-ai/scientific-agent-skills" --skill "k-dense-ai/scientific-agent-skills/dask"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/k-dense-ai/scientific-agent-skills" that are relevant to the current task. Run `npx skills add "https://github.com/k-dense-ai/scientific-agent-skills"` and select the relevant skills, then follow their instructions.