Skill UI

This toolkit provides comprehensive processing and analysis capabilities for various physiological biosignals, including ECG, EEG, EDA, PPG, and EMG. It is designed for advanced psychophysiology research, clinical diagnosis, and human-computer interaction studies, supporting complex analyses like HRV, microstate detection, and autonomic assessment.

A comprehensive toolkit for analyzing high-density Neuropixels neural recordings. It supports the entire electrophysiology workflow, from raw data loading (SpikeGLX, Open Ephys) and preprocessing (filtering, motion correction) to advanced spike sorting (Kilosort4) and rigorous quality metric calculation. Features include Allen/IBL unit curation and AI-assisted visualization for publication-ready results.

Comprehensive Python integration for OMERO, an open-source platform designed for managing, visualizing, and analyzing complex microscopy datasets. This skill allows users to programmatically access images, retrieve structured datasets, manage annotations (ROIs, tags), perform deep pixel analysis, and execute batch processing for high-content screening and advanced bioimaging workflows.

PathML is a comprehensive Python toolkit designed for advanced computational pathology workflows. It facilitates the end-to-end analysis of whole-slide pathology images (WSI) from over 160 proprietary formats. Key functionalities include image preprocessing (stain normalization, tissue detection), constructing spatial cell/tissue graphs, and training deep learning models (e.g., HoVer-Net). It is highly specialized for analyzing multiparametric data from technologies like CODEX and Vectra.

polars-bio is a high-performance Python library built on Polars for genomic interval operations and bioinformatics file I/O. It provides a familiar DataFrame-centric API for interval arithmetic (overlap, nearest, merge, complement, etc.) and supports reading/writing major bioinformatics formats like BED, VCF, BAM, and GFF. Key features include streaming, cloud-native I/O, and SQL query support, making it a fast alternative for large-scale genomic data processing.

PyDESeq2 is a robust Python implementation designed for differential gene expression analysis using bulk RNA-seq count data. It provides a comprehensive, end-to-end bioinformatics workflow, guiding users from data loading and design specification (including multi-factor effects) through the core DESeq2 fitting process. The tool performs Wald tests, applies FDR correction, and offers advanced features like LFC shrinkage, making it essential for integrating complex genomics analysis into Python-based pipelines.

PyOpenMS is a comprehensive platform providing Python bindings to the OpenMS library. It is designed for advanced mass spectrometry data analysis, supporting entire proteomics and metabolomics workflows. Key capabilities include handling diverse file formats (mzML, mzXML), signal processing, robust feature detection, and precise peptide/protein identification. It is ideal for quantitative biological research, allowing users to process complex LC-MS/MS pipelines.

Pysam is a powerful Python module designed for reading, manipulating, and writing core genomic file formats. It provides a Pythonic interface to htslib, enabling users to process alignment files (BAM/CRAM), variant calls (VCF/BCF), and reference sequences (FASTA/FASTQ). Use it for implementing complex bioinformatics pipelines, calculating coverage, quality control, and detailed variant annotation.

This skill acts as a dedicated scientific ideation partner, guiding the user through a structured five-phase brainstorming process. It is ideal for early-stage research, helping to generate novel hypotheses, exploring interdisciplinary connections, challenging underlying assumptions, and identifying critical research gaps. It facilitates creative problem-solving across various scientific domains.

This skill utilizes Google's TimesFM foundational model for zero-shot time series forecasting. It processes any univariate time series (e.g., sales, sensor data, weather) without requiring custom training. Users receive point forecasts alongside calibrated prediction intervals (quantiles), enabling robust probabilistic analysis. The skill includes a mandatory preflight checker to validate system resources (RAM/GPU/Disk) before model loading.

UMAP (Uniform Manifold Approximation and Projection) is a powerful non-linear dimensionality reduction technique used for visualizing high-dimensional data and preprocessing for clustering. It preserves both local and global data structures, offering fast, scalable embeddings. Users can leverage its supervised capabilities by incorporating class labels, making it suitable for advanced machine learning pipelines and data exploration.

Vaex is a high-performance Python library designed for lazy, out-of-core DataFrames. It enables processing and visualization of massive tabular datasets (billions of rows) that exceed available RAM. Users can perform fast statistics, aggregations, and build robust ML pipelines on big data files (CSV, Parquet, HDF5) without relying on distributed clusters, making it ideal for single-machine, large-scale analytics.