Skill UI

NetworkX is a powerful Python library used for creating, manipulating, and analyzing complex networks and graphs. It supports graph structures (directed/undirected, multi-edges) and provides extensive algorithms for tasks such as finding shortest paths, calculating centrality measures (e.g., PageRank), detecting communities, and simulating various network types (e.g., scale-free, small-world). Ideal for social science, biology, and transportation network modeling.

A comprehensive toolkit for securely inspecting and automating complex microscopy data workflows against OMERO.server. It provides deep programmatic access using omero-py, covering metadata, annotations, ROIs, rendering, and detailed data export/import planning. Essential for building controlled, large-scale data inventory and analysis pipelines.

This comprehensive skill provides a standardized pipeline for reconstructing the evolutionary history of biological sequences (genes, proteins, genomes). It guides users through essential steps, including multiple sequence alignment using MAFFT, maximum likelihood tree inference with IQ-TREE 2, and visualization with ETE3. Ideal for research in viral phylodynamics, molecular clock studies, and large-scale comparative genomics.

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.

This skill provides programmatic access to the PrimeKG, a comprehensive knowledge graph integrating over 20 primary databases. It facilitates multiscale biological research by mapping complex relationships between genes, drugs, diseases, and phenotypes. Use it for drug repurposing, identifying novel disease targets, and analyzing molecular mechanisms across various biological scales.

PyDESeq2 is a robust Python implementation for conducting differential gene expression analysis on bulk RNA-seq count data. It supports complete statistical workflows, including data loading from various formats, complex multi-factor design specifications (e.g., accounting for batch effects or covariates), normalization, and performing advanced Wald tests. It is ideal for researchers transitioning R-based DESeq2 analyses into scalable Python pipelines.

A comprehensive, scalable Python toolkit designed for end-to-end single-cell RNA-seq data analysis. It supports crucial steps including quality control (QC), normalization, dimensionality reduction (PCA, UMAP, t-SNE), cell clustering, and differential gene expression analysis. The skill provides ready-to-run scripts for analyzing various formats (e.g., .h5ad, 10X, R-native) and generating publication-quality visualizations and cell type annotations.

scVelo is a powerful Python package designed for RNA velocity analysis using single-cell RNA-seq data. It estimates cell state transitions by modeling the kinetics of mRNA splicing—specifically using the ratio of unspliced to spliced mRNA abundances. This allows researchers to reconstruct developmental trajectories and predict cell fates, even when only snapshot data is available, making it a vital complement to standard trajectory inference tools like Scanpy.

UMAP (Uniform Manifold Approximation and Projection) is a powerful technique for non-linear dimensionality reduction. This skill enables the creation of scalable, structure-preserving embeddings (2D/3D) for visualization, clustering preprocessing, and advanced machine learning feature engineering. It supports various metrics (e.g., Euclidean, Cosine) and supervised learning workflows.

A comprehensive Python wrapper for the U.S. Treasury Fiscal Data REST API. This tool allows users to query diverse federal financial datasets, including national debt, interest rates, foreign exchange rates, and government revenue/spending statistics. It requires no API key and is ideal for financial modeling, economic research, and data analytics projects.

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.

A comprehensive guide to reducing expenses on ClickHouse Cloud. Learn advanced techniques for storage optimization (compression, TTL), compute resource tuning (scaling, thread limits), and query efficiency improvements. Use this when managing data budgets or analyzing high ClickHouse billing charges.