Skill UI

gget is a comprehensive command-line and Python package providing unified access to over 20 major bioinformatics databases. Users can perform deep analyses across genomics, proteomics, and disease associations. Key functions include gene information lookups, sequence alignment (BLAST), structural prediction (AlphaFold), single-cell analysis, and enrichment studies, supporting both interactive queries and automated pipelines.

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.

Histolab is a specialized Python library for digital pathology designed to process Whole Slide Images (WSI). It automates critical steps including sophisticated tissue detection, customizable tile extraction (random, grid, score-based), and stain normalization. It is essential for preparing high-quality, structured datasets for deep learning model training and comprehensive quantitative whole slide analysis.

A comprehensive framework for transforming raw observations into rigorous, evidence-bounded, and testable research plans. It guides users through establishing research questions (PICO/PECO), formulating candidate hypotheses and rival explanations, defining causal estimands, and creating preregistration-ready analysis plans, all while maintaining strict ethical and scientific guardrails.

This skill guides the creation of highly auditable, evidence-traceable market research reports. It provides a structured workflow for defining market scope, mapping every claim to primary sources (like official filings and statistics), and conducting rigorous, assumption-driven market sizing (TAM/SAM/SOM) and forecasting scenarios. Essential for professional market analysis and strategic planning.

MarkItDown is a lightweight utility designed to convert heterogeneous documents (PDF, Office files, HTML, etc.) into structured, plain Markdown. It is specifically optimized for text analysis, search indexing, and preparing data for Large Language Models (LLM) and Retrieval-Augmented Generation (RAG) systems, prioritizing content structure over visual fidelity. Supports local, stream, and advanced multimodal extraction scenarios.

A comprehensive guide for designing, reviewing, and migrating numerical workflows in MATLAB or GNU Octave. It emphasizes building reproducible projects, adhering to strict safety boundaries, and ensuring code semantics are explicit for robust data processing and analysis.

A comprehensive toolkit for running and analyzing molecular dynamics (MD) simulations. This skill covers the entire workflow: system setup, force field definition, energy minimization, and equilibration using industry-standard tools like OpenMM and MDAnalysis. Essential for structural biology, drug binding studies, and biophysics research.

A comprehensive research toolkit for building and auditing reproducible workflows for physiological time-series data. It specializes in preprocessing, event/interval analysis, multimodal alignment, and extracting variability and complexity features from signals like ECG and EDA. Designed strictly for academic and research use, emphasizing rigorous data contracts and method-aware validation, while strictly disclaiming any diagnostic or medical device validation role.

A comprehensive end-to-end toolkit for analyzing high-density Neuropixels extracellular recordings. It guides the user through the complete electrophysiology workflow, including data loading (SpikeGLX, NWB), robust preprocessing, motion/drift correction, advanced spike sorting (Kilosort4, etc.), quality metric calculation, and AI-assisted unit curation. Ideal for generating publication-ready results from large neural datasets.

This skill functions as an expert GPU optimization engineer, transforming CPU-bound Python code into highly accelerated GPU kernels. It provides expertise across scientific computing, deep learning, and data analytics, covering core libraries like NumPy, pandas, and scikit-learn. Use cases include massive data parallelism, geospatial analysis, physics simulations, image processing, and advanced vector search on NVIDIA hardware.

PathML is a robust computational platform engineered for local, research-only workflows in computational pathology. It facilitates comprehensive analysis pipelines, including whole slide image (WSI) loading and tiling, preprocessing, quality control (QC), quantification of multiplex images, and the construction of spatial graphs. Designed with strict adherence to data privacy and research reproducibility, it supports advanced biological data analysis.