skills/claude-scientific-skills
1
0
Fork 0
mirror of https://github.com/K-Dense-AI/claude-scientific-skills.git synced 2026-03-28 07:33:45 +08:00
Code Issues Packages Projects Releases Wiki Activity

Fix descriptions to adhere to character limits

Browse Source
This commit is contained in:
Timothy Kassis
2025-10-21 09:33:30 -07:00
parent 163d6b9d6b
commit 32109101d0
83 changed files with 83 additions and 83 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
scientific-packages/dask/SKILL.md
View File

@@ -1,6 +1,6 @@
---
name: dask
description: "Toolkit for parallel and distributed computing in Python enabling larger-than-memory operations, parallel processing, and distributed computation. Use this skill when: (1) datasets exceed available RAM and need chunked processing, (2) pandas/NumPy operations are slow and need parallelization across cores, (3) processing multiple files (CSV, Parquet, JSON, logs) that collectively exceed memory, (4) building custom parallel workflows with task dependencies, (5) scaling from prototype pandas/NumPy code to production on larger data, (6) need distributed computing across multiple machines, (7) working with scientific datasets (HDF5, Zarr, NetCDF) larger than memory, (8) ETL pipelines processing terabytes of unstructured data, (9) parameter sweeps or embarrassingly parallel computations, (10) when simpler solutions (better algorithms, efficient formats, sampling) aren't sufficient. Supports DataFrames (parallel pandas), Arrays (parallel NumPy), Bags (parallel Python lists), Futures (task-based parallelization), and various schedulers. NOT suitable for: small datasets fitting in memory, single-file processing, simple computations without parallelism needs, when NumPy/Pandas already perform adequately, or when task overhead exceeds computation time."
description: "Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows."
---
# Dask