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 Python library for parallel and distributed computing that scales pandas, NumPy, and custom workflows beyond memory limits. It supports DataFrames, Arrays, and Bags for processing large datasets across single machines or clusters. Ideal for scientific computing, ETL tasks, and parallelizing operations when data exceeds available RAM or requires distributed execution.

Performs bounded local exploratory analysis of supported scientific files, including CSV/TSV/JSON profiles, optional NumPy, HDF5, FASTA/FASTQ and image metadata inspection, audits, and EDA report scaffolds.

Read, inspect, and write Flow Cytometry Standard (FCS) 2.0, 3.0, and 3.1 files with FlowIO. Extract HEADER, TEXT, ANALYSIS, and channel metadata, retrieve event data as NumPy arrays, handle multi-dataset legacy files, and export FCS 3.1 list-mode data. For compensation, transforms, or gating, use FlowKit instead.

Accelerate scientific Python code on NVIDIA hardware using CUDA and RAPIDS libraries. Optimizes NumPy, pandas, scikit-learn, and ML workloads for GPU execution. Includes profiling, memory management, and kernel optimization to ensure correctness and performance gains.

Store and process large N-dimensional arrays with chunking, compression, and parallel I/O. Supports cloud stores like S3/GCS via fsspec and integrates with NumPy, Dask, and Xarray for scientific pipelines.

Build financial models, backtest trading strategies, and analyze market data with risk metrics, portfolio optimization, and statistical arbitrage using pandas, numpy, and scipy.