Skill UI

An autonomous research agent that plans complex investigations using Directed Acyclic Graphs (DAGs). It executes sub-questions in parallel, performs gap analysis, and iteratively refines findings before synthesizing a comprehensive, structured report. Ideal for deep market analysis, academic literature reviews, or complex problem-solving requiring multiple data sources.

deepTools is a comprehensive suite of command-line tools for analyzing high-throughput sequencing data. It supports quality control (QC), normalization, sample comparison, and generating publication-quality visualizations for various experiments, including ChIP-seq, RNA-seq, and ATAC-seq. Key functions include converting BAM to bigWig, calculating correlations, and generating heatmaps/profiles around genomic features.

This tool provides access to the Cancer Dependency Map (DepMap) data, derived from genome-wide CRISPR screens, RNAi, and compound sensitivity assays. It allows users to query gene dependency scores, identify cancer-specific vulnerabilities, predict synthetic lethal interactions, and validate potential oncology drug targets across various cancer cell lines.

A comprehensive cloud platform for biomedical data analysis and genomics research. This skill provides tools for building, deploying, and managing complex bioinformatics pipelines (apps/applets). Users can manage diverse data types (FASTQ, BAM, VCF), execute advanced workflows, and interact with the platform programmatically using the dxpy Python SDK.

ETE is a comprehensive toolkit for phylogenomic and hierarchical tree analysis. It enables users to load and manipulate diverse tree formats (Newick, NHX), detect key evolutionary events like speciation and duplication, identify orthologs and paralogs, and integrate with NCBI taxonomy databases. The toolkit supports professional visualization in PDF/SVG for high-quality scientific publications.

Perform deep exploratory data analysis (EDA) across a vast array of scientific formats, including chemistry, bioinformatics, microscopy, and spectroscopy. This skill automatically detects the file type, assesses data quality and integrity, extracts format-specific metadata, and generates detailed markdown reports. It is ideal for understanding the structure, content, and potential analysis paths for complex scientific datasets.

Geniml is a comprehensive Python package designed for applying machine learning techniques to genomic interval data (BED files). It provides advanced, unsupervised methods for learning high-dimensional embeddings of genomic regions, single cells (scATAC-seq), and metadata. Core functionalities include Region2Vec for region embeddings, BEDspace for joint region-label analysis, scEmbed for single-cell data, and utilities for building consensus peak universes, enabling robust clustering and feature extraction in genomics research.

A powerful skill covering geospatial science, including GIS, remote sensing, spatial statistics, and machine learning for Earth observation. It supports processing of various data types (raster, vector, point cloud) from sources like Sentinel and Landsat, and integrates modern cloud-native workflows (STAC, COG). Ideal for environmental monitoring, urban planning, and advanced spatial modeling tasks.

GeoPandas extends pandas to enable powerful spatial operations on vector data. It is essential for tasks in Geographic Information Systems (GIS), allowing users to read, write, and analyze multiple formats like GeoJSON, Shapefile, and GeoPackage. Key functionalities include spatial joins, calculating geometric properties (e.g., area, buffer), reprojecting coordinates (CRS management), and creating interactive choropleth maps. Use it for advanced spatial analysis, such as overlay operations, dissolving boundaries, and nearest neighbor lookups across different datasets.

This skill automatically detects all available computational resources, including CPU core counts, GPU support (NVIDIA, AMD, Apple Silicon), memory capacity, and disk space. It generates a detailed JSON report that goes beyond simple metrics by providing strategic, context-aware recommendations. These recommendations guide users on the optimal computational approach—such as choosing between Dask for out-of-core processing, selecting the right GPU backend, or determining the ideal number of workers for parallel processing—before running any intensive scientific or data analysis task.

gget is a comprehensive command-line interface and Python library providing unified access to over 20 major bioinformatics databases. It allows researchers to perform complex tasks such as gene information retrieval, sequence analysis (BLAST, BLAT), structural modeling, and enrichment analysis using a consistent API. Ideal for interactive data exploration, quick lookups, and integrating bioinformatics workflows into Python scripts.

This skill provides comprehensive tools for analyzing and engineering protein glycosylation patterns. It scans sequences for canonical N-glycosylation sequons (N-X-S/T), predicts O-glycosylation hotspots, and supports rational modification. It is essential for optimizing therapeutic antibodies, improving protein stability, or designing complex vaccine antigens.