Skill UI

This guide provides comprehensive patterns and best practices for designing high-impact Key Performance Indicator (KPI) dashboards. It covers everything from establishing a KPI framework (Strategic, Tactical, Operational) to defining departmental metrics (Sales, Marketing, Finance) and implementing best-practice layouts for executive summaries and SaaS metrics. Use this skill to translate raw data into actionable business insights.

High-performance Rust toolkit with Python bindings for genomic interval analysis, covering overlap detection, coverage track generation, ML tokenization, reference sequence handling, fragment processing, and scoring workflows for sequencing datasets.

Vaex is a high-performance Python library for lazy out-of-core DataFrames that process billions of rows without RAM. Use it to analyze massive CSV/HDF5/Arrow/Parquet tables, compute fast statistics, visualize big data, and build scikit-learn pipelines.

A comprehensive toolkit designed to track and analyze content performance, engagement metrics, and Return on Investment (ROI) across major social media platforms. By utilizing specialized Apify Actors, this skill extracts deep insights from Instagram, Facebook, YouTube, TikTok, and more, helping users identify high-performing content, monitor growth, and measure marketing campaign effectiveness.

A comprehensive expert prompt for evaluating Power BI data models. It guides the assessment of model architecture (Star Schema, relationships), storage optimization (Import, DirectQuery, Dual), data quality, performance scalability, and governance best practices. Use this when reviewing an existing BI model to ensure it is robust, maintainable, and highly performant.

Gtars is a high-performance toolkit written in Rust with Python bindings, designed for comprehensive analysis of genomic interval data. It provides specialized modules for overlap detection, generating coverage tracks (WIG/BigWig), tokenization for Machine Learning models, and detailed fragment analysis in single-cell genomics. Use it for robust bioinformatics pipelines and computational genomics research.

Perform comprehensive molecular dynamics (MD) simulations using high-performance engines like OpenMM and analysis tools like MDAnalysis. This skill allows users to model the time evolution of biological systems, covering system preparation (solvation, force field assignment), energy minimization, NVT equilibration, and advanced analysis (RMSD, free energy surfaces). Ideal for structural biology, drug binding studies, and biophysics research.

Polars is a lightning-fast DataFrame library built on Apache Arrow for Python. It provides an expression-based API, lazy query optimization, and parallel computation capabilities for efficient data manipulation, analytics, and ETL pipelines. It is designed to handle large, memory-intensive datasets, making it an ideal alternative for migrating from pandas or optimizing complex data workflows.

A high-performance library built on Polars for advanced genomic interval operations and bioinformatics file I/O. It provides robust support for core interval arithmetic (overlap, merge, complement) and reading/writing major formats like VCF, BAM, BED, and GFF. Features include out-of-core streaming, cloud-native I/O (S3, GCS, Azure), and a powerful SQL interface, making it ideal for large-scale genomic research.

Vaex is a high-performance Python library designed for processing and visualizing enormous tabular datasets that exceed available RAM (gigabytes to terabytes). It excels at out-of-core DataFrame operations, lazy evaluation, fast statistical aggregation, and machine learning integration on massive datasets. Use it for analyzing large CSV, Parquet, or HDF5 files efficiently on a single machine, without memory constraints.

Zarr is a robust Python library designed for storing massive N-dimensional arrays. It supports efficient chunking, compression, and parallel I/O, making it ideal for large-scale scientific computing pipelines. Seamlessly integrates with NumPy, Dask, and Xarray, and supports cloud storage backends like S3 and GCS via fsspec, ensuring high performance in cloud-native environments.

This skill connects to the DeepEar Lite platform to fetch real-time, high-frequency financial signals. It retrieves crucial data points including signal titles, sentiment summaries, confidence scores, and detailed reasoning. Use this when you require immediate, actionable insights into volatile market trends, stock performance factors, or comprehensive financial analysis.