Skill UI

A specialist tool designed to generate, format, and validate comprehensive technical documentation. It supports multiple standards, including Google/NumPy docstrings (for Python), JSDoc (for TypeScript), and full OpenAPI/Swagger specifications. Key features include automated detection of code languages and frameworks, robust validation of code examples (using tools like doctest and OpenAPI linters), and generating structured user guides or site configurations.

Dask is a Python library that scales pandas/NumPy workflows beyond RAM by parallelizing DataFrames, Arrays, Bags, and custom tasks across cores or clusters for ETL, log analysis, and distributed ML.

FlowIO reads and writes Flow Cytometry Standard (FCS) files, exposing metadata, channels, and NumPy event arrays while allowing CSV/DataFrame conversion, making it ideal for cytometry pipelines and backend preprocessing services.

Zarr is a Python toolkit for chunked N-D array storage with compression, sharding, and native integrations with NumPy/Dask/Xarray, ideal for parallel I/O and scalable cloud pipelines.

Provides a brief overview placeholder for the Numpy skill, indicating the module while noting that the detailed information could not be retrieved from the manifest.

Accelerates CPU-bound Python workflows by translating NumPy, pandas, ML, graph, image, simulation, and IO code into NVIDIA GPU kernels using CuPy, Numba CUDA, Warp, cuDF/cuML/cuGraph, KvikIO, cuSpatial, and RAFT for dramatic speedups.

Dask is a powerful Python library designed for scalable, parallel, and distributed computing. It enables workflows for datasets that exceed available RAM or require processing across multiple machines. Users can scale familiar pandas and NumPy operations (DataFrames, Arrays) and process unstructured data (Bags) efficiently, making it ideal for large-scale ETL, analytics, and distributed machine learning tasks.

FlowIO is a specialized, low-level library designed for reading, inspecting, and writing Flow Cytometry Standard (FCS) files (versions 2.0, 3.0, and 3.1). It allows users to extract event data as NumPy arrays, inspect detailed metadata (HEADER, TEXT, ANALYSIS), and handle multi-dataset structures. It is crucial for data preparation, format conversion, and foundational metadata management in bioinformatics pipelines, but does not perform complex analysis like gating or compensation.

This skill functions as an expert GPU optimization engineer, transforming CPU-bound Python code into highly accelerated GPU kernels. It provides expertise across scientific computing, deep learning, and data analytics, covering core libraries like NumPy, pandas, and scikit-learn. Use cases include massive data parallelism, geospatial analysis, physics simulations, image processing, and advanced vector search on NVIDIA hardware.

Zarr is a robust Python library designed for efficiently storing and managing large N-dimensional arrays. It provides features like chunking, compression, and parallel I/O, making it perfectly suited for cloud-native scientific computing pipelines. It ensures seamless integration with major scientific stacks including NumPy, Dask, and Xarray, while supporting remote storage backends like S3 and GCS.