Skill UI

This skill provides a comprehensive workflow to transform raw data into professional visual charts. It intelligently analyzes data features (e.g., trends, comparisons, flows) to select the optimal chart type (line, bar, pie, etc.). Users provide data and specifications, and the skill generates a high-quality, detailed chart image using a structured JavaScript process.

This skill provides comprehensive management for annotation schemas (configs) and human review workflows (queues) within the Arize platform. It supports defining categorical, continuous, and freeform label types. Users can programmatically apply these annotations to project spans and datasets using the Python SDK, facilitating structured human feedback loops and improving model monitoring.

A powerful toolset for automating core PostHog product analytics tasks via Rube MCP. It enables developers and analysts to capture user events, list and filter event data, manage feature flags (creation, retrieval, listing), and inspect user profiles. Ideal for integrating advanced product tracking and A/B testing workflows directly into development pipelines.

This skill safely randomizes the order of repetitive JSON objects within an array without corrupting the data or breaking the JSON syntax. It acts as a data engineer, rigorously validating the input schema (checking for property consistency and lack of nesting) before performing the shuffle. It ensures data integrity is maintained while reordering records, making it ideal for testing, simulation, or data preparation workflows.

This skill provides comprehensive commands to manage the entire AI model evaluation lifecycle using Arize. Users can list, retrieve metadata, and export results from experiments. It is ideal for comparing multiple model runs, benchmarking performance against a dataset, and analyzing model accuracy using structured evaluation workflows (CRUD operations).

AnnData is a specialized Python package designed for managing and analyzing annotated matrices, particularly crucial in single-cell genomics and bioinformatics. It efficiently stores experimental measurements (X) alongside comprehensive metadata (obs, var, obsm, etc.). This skill allows users to read, write, subset, and concatenate large, complex datasets in formats like .h5ad and .zarr, making it essential for scRNA-seq analysis workflows and integration with the scverse ecosystem.

A comprehensive Python package providing a unified interface to over 40 major bioinformatics web services and databases. Use it for complex biological workflows, including cross-database querying (e.g., UniProt, KEGG, ChEMBL), identifier mapping, protein sequence retrieval, pathway analysis, and running sequence similarity searches like BLAST. It streamlines data integration from multiple life science resources into consistent Python code.

Dask is a powerful Python library designed for parallel and distributed computing. It allows users to scale existing pandas and NumPy workflows beyond the limits of available RAM, processing massive datasets up to the terabyte scale. It provides specialized APIs (DataFrames, Arrays, Bags) for handling structured, scientific, and unstructured data efficiently across single machines or multi-node clusters.

DeepTools is a comprehensive command-line toolkit for processing and analyzing high-throughput sequencing data. It supports workflows for ChIP-seq, RNA-seq, ATAC-seq, and other NGS experiments. Core capabilities include converting BAM files to normalized coverage tracks (bigWig), performing quality control (QC), sample correlation analysis, and generating publication-quality visualizations, such as heatmaps and profile plots across genomic regions.

A lightweight Python library designed for reading, parsing, and writing Flow Cytometry Standard (FCS) files. It extracts event data as NumPy arrays, handles complex metadata, and supports multiple FCS versions (2.0-3.1). Ideal for data preprocessing, building cytometry pipelines, and inspecting experimental results.

A comprehensive toolkit for advanced geospatial analysis. It covers remote sensing (Sentinel, Landsat), GIS operations (vector/raster/point cloud), spatial statistics, and Machine Learning for Earth Observation. Supports cloud-native workflows, multiple data types, and 8 programming languages, making it ideal for complex scientific research and commercial spatial projects.

This skill proactively detects and reports comprehensive system resources, including CPU cores, GPU types (NVIDIA, AMD, Apple Silicon), available memory, and disk space. It generates a detailed JSON report with strategic, context-aware recommendations for optimizing scientific computing workflows, such as recommending parallel processing libraries (Dask, joblib), out-of-core methods (Zarr), or appropriate GPU acceleration backends (Metal, ROCm). Use this before any large-scale data analysis, model training, or computationally intensive simulation to make informed architectural decisions.