Skill UI

gget is a versatile CLI and Python package designed for rapid, reproducible bioinformatics searches across major genomic reference databases (e.g., Ensembl, UniProt). It allows users to quickly query gene metadata, perform initial BLAST/BLAT style alignments, and explore associated pathways, cancer, or disease modules. It is ideal for initial data exploration and generating reproducible evidence logs before building complex, full-scale local pipelines.

Matplotlib is the foundational Python library for creating static, animated, and high-quality visualizations. This comprehensive skill guide covers everything from basic line plots and scatter graphs to advanced concepts like multi-subplot layouts, heatmaps, and 3D visualization. It details both the convenient pyplot interface and the powerful object-oriented API, ensuring you can generate publication-ready charts with professional customization.

NetworkX is a powerful Python package designed for creating, manipulating, and analyzing complex networks and graphs. It supports various graph types (Directed, Undirected, MultiGraphs) and provides extensive functionality for graph algorithms, including shortest path calculation, centrality measures (like PageRank), community detection, and connectivity checks. Users can generate synthetic networks, read/write data from multiple formats (GraphML, CSV, Pandas), and visualize results using Matplotlib. Ideal for social network analysis, biological research, and system modeling.

Polars is a lightning-fast DataFrame library designed for efficient, memory-efficient data processing in Python and Rust. Built upon Apache Arrow, it supports lazy evaluation and parallel execution, making it ideal for ETL pipelines, large-scale data transformations, and scenarios where standard Pandas performance is insufficient. It excels at handling datasets up to 100GB that fit within available RAM.

Scanpy is a powerful, scalable Python toolkit built on AnnData for comprehensive single-cell RNA-seq data analysis. It supports the complete biological workflow, including quality control, normalization, dimensionality reduction (PCA, UMAP, t-SNE), clustering, marker gene identification, and sophisticated cell type annotation, enabling the generation of publication-quality figures.

Seaborn is a high-level Python visualization library designed for creating publication-quality statistical graphics. It facilitates dataset-oriented plotting, multivariate analysis, and complex statistical visualizations (like KDEs, distributions, and relationships) directly from Pandas DataFrames with minimal code. It builds upon Matplotlib to provide semantic mapping and statistical awareness.

Statsmodels is Python's premier library for rigorous statistical modeling. It provides comprehensive tools for estimation, inference, and diagnostics across a wide range of statistical methods. Use this skill for advanced analysis, including linear regression (OLS, WLS), generalized linear models (Logistic, Poisson), and complex time series forecasting (ARIMA). Ideal for econometric studies and producing publication-ready results.

This skill serves as a deterministic internal downloader for image URLs extracted from comprehensive web search results. It receives normalized media slots and search output via stdin, utilizing Python HTTP APIs to download direct image links. The process includes validating MIME types and saving files accurately into the configured local project directory, ensuring reliable asset retrieval without relying on sandboxed shell tools.