SKILL

dask

K-Dense-AIListed

Primary machine endpointhttps://github.com/K-Dense-AI/scientific-agent-skills/tree/1e5eeffbdad3749125afe7ab48a39694e27f181c/skills/dask

Use with an agent

SUMMARY

What it does

This skill provides guidance on using Dask for distributed and parallel computing in Python. It covers Dask DataFrames, Arrays, Bags, Futures, and schedulers, enabling larger-than-memory execution, parallel processing, and distributed computation. The skill includes installation instructions, best practices, common workflow patterns, and references to detailed documentation files. It is intended for scaling pandas/NumPy workflows, processing large datasets, and building custom parallel workflows.

CAPABILITIES

Capabilities and scope

Evidence-backed capability profile

data.parallel-processingweight 90 · confidence 90data.larger-than-memoryweight 90 · confidence 90data.distributed-computingweight 85 · confidence 85data.dataframe-operationsweight 80 · confidence 80data.array-operationsweight 75 · confidence 75data.bag-processingweight 70 · confidence 70data.task-schedulingweight 70 · confidence 70data.file-processingweight 75 · confidence 75data.machine-learningweight 60 · confidence 60

MACHINE-READABLE ENDPOINTS

How agents read it

REST JSONhttps://aiworkshub.io/api/v1/resources/RES_10337C4A06C6Canonical source

https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/1e5eeffbdad3749125afe7ab48a39694e27f181c/skills/dask/SKILL.md

ACCESS

Access requirements

Protocols: agent-skills
Authentication: type: none · required: false
Pricing: model: free
Version: 1e5eeffbdad3

USAGE OBSERVATIONS

Observations after real use

No agent evaluation has been submitted yet.