Skill UI

The ETE Toolkit is a powerful Python library designed for comprehensive analysis, manipulation, comparison, and visualization of phylogenetic and hierarchical trees. It supports reading and writing standard formats like Newick and Nexus, enabling advanced operations such as topology editing, calculating Robinson-Foulds distances, detecting speciation/duplication events, and annotating nodes. It also integrates with major biological databases (NCBI/GTDB taxonomy) and provides interactive visualization capabilities. Note: This tool processes existing trees; it does not perform raw sequence al...

This skill performs comprehensive Exploratory Data Analysis (EDA) on specialized scientific datasets. It supports over 200 file formats across diverse domains, including chemistry (PDB), bioinformatics (FASTA, BAM), microscopy (TIFF), and spectroscopy. The system automatically detects the file type, assesses data quality and integrity, and generates detailed, format-specific markdown reports, providing deep insights and recommending downstream analyses.

FlowIO is a specialized Python utility designed for low-level reading, inspecting, and writing of Flow Cytometry Standard (FCS) files (versions 2.0, 3.0, and 3.1). It allows users to extract event data as NumPy arrays, inspect complex metadata (HEADER, TEXT, ANALYSIS), and handle multi-dataset files. This tool is ideal for data preparation, metadata inventory, and conversion into formats suitable for downstream bioinformatics analysis, though it does not perform complex analysis like gating or compensation.

Geniml is a comprehensive Python package designed for applying machine learning models to genomic interval data, typically stored in BED files. It provides several unsupervised methods—including Region2Vec and BEDspace—to learn robust embeddings of genomic regions, single cells (scATAC-seq), and associated metadata. Use cases include dimensionality reduction, structural variation analysis, cell-type annotation, and cross-modal querying in genomics research.

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.

A powerful Python library that extends pandas to enable complex spatial operations on vector geometric data. It supports reading and writing diverse formats, including Shapefile, GeoJSON, and GeoPackage. Users can perform advanced GIS tasks such as spatial joins, buffering, overlay analysis, calculating areas, managing Coordinate Reference Systems (CRS), and generating interactive choropleth maps, making it essential for geographic data science projects.

gget is a comprehensive command-line tool and Python package offering unified access to over 20 genomic databases. It facilitates quick lookups for gene information, sequence analysis (BLAST), protein structures, and expression data, supporting both interactive CLI use and advanced programmatic Python workflows for biological research.

This skill provides comprehensive tools to analyze and engineer protein glycosylation patterns. It scans amino acid sequences for canonical N-glycosylation sequons (N-X-S/T) and predicts O-glycosylation hotspots. Use cases include optimizing therapeutic antibodies, designing vaccine antigens, and characterizing biosimilars by rationally modifying glycan structures for improved stability, function, or immune evasion.

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.

Histolab is a specialized Python library for digital pathology research, enabling robust processing of Whole Slide Images (WSI). It automates complex workflows including tissue detection, sophisticated segmentation, and flexible tile extraction from various formats (SVS, TIFF). This tool is crucial for preparing structured, high-quality datasets required for deep learning models and advanced computational pathology analysis.

Hypogenic is an advanced framework that uses Large Language Models (LLMs) to systematically generate and test hypotheses on complex tabular datasets. It supports three powerful modes: data-driven generation (HypoGeniC), literature-grounded refinement (HypoRefine), and combined union methods. This tool is essential for accelerating scientific discovery in fields such as predictive modeling, content analysis, and deception detection by combining empirical data insights with theoretical knowledge.

This skill guides users through the systematic scientific method, helping to formulate testable, evidence-based hypotheses from initial observations or complex datasets. It structures the process by conducting literature reviews, synthesizing conflicting evidence, developing multiple competing hypotheses, and critically evaluating their quality (testability, falsifiability). All final reports mandate the inclusion of AI-generated scientific schematics to visualize complex mechanisms, experimental designs, and causal relationships, ensuring publication-quality output.