Skill UI

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.

This comprehensive toolkit facilitates the entire phylogenetic analysis workflow, from sequence alignment to tree inference. It integrates industry-standard tools like MAFFT for multiple sequence alignment, TrimAl for alignment trimming, and powerful maximum likelihood methods using IQ-TREE 2 or FastTree. Ideal for molecular genomics, tracing viral phylodynamics, studying protein evolution, or conducting molecular clock analysis. Built using Python for seamless integration.

PyHealth is a modular Python toolkit designed for developing deep learning pipelines in clinical and healthcare settings. It provides a standardized 5-stage framework (Dataset → Task → Model → Trainer → Metrics) to handle complex, multi-modal data, including EHR records, physiological signals, and medical imaging. Use cases include predicting mortality, readmission risks, recommending drugs, or performing medical code mapping (ICD/ATC).

Qiskit is a comprehensive SDK for developing quantum algorithms. It enables users to build, optimize, simulate, and execute quantum circuits using modern APIs (Qiskit 2.x). It supports local exact sampling, high-performance noisy simulation via Qiskit Aer, and direct execution on IBM Quantum hardware through the Runtime service, making it a powerful tool for quantum research and development.

RDKit is a comprehensive cheminformatics library providing robust Python APIs for molecular structure manipulation and analysis. It supports tasks ranging from molecular I/O (SMILES, SDF) and calculating physical descriptors (MW, LogP) to advanced substructure searching, 3D coordinate generation, and chemical reaction modeling. It is an essential toolkit for drug discovery, computational chemistry, and academic cheminformatics research.

A comprehensive Python toolkit built on AnnData for analyzing single-cell RNA-seq data. It supports the complete workflow, including quality control (QC), normalization, dimensionality reduction (PCA, UMAP, t-SNE), clustering, marker identification, and cell type annotation. Users benefit from a suite of ready-to-run CLI scripts that guide the entire process from raw data loading to publication-quality visualization.

scikit-bio is a comprehensive Python library designed for advanced bioinformatics and biological data analysis. It provides robust tools for sequence manipulation (DNA, RNA, Protein), performing sophisticated sequence alignments (global, local, multiple), constructing and analyzing phylogenetic trees, and conducting complex ecological statistics. Key features include calculating diversity metrics (alpha/beta), running ordination analyses (PCoA), and handling diverse biological file formats (FASTA, Newick, BIOM). Ideal for researchers in microbiology, genomics, and ecology.

This skill provides comprehensive guidance for classical machine learning using scikit-learn, the industry-standard Python library. It covers the full ML lifecycle, including supervised tasks (classification, regression), unsupervised tasks (clustering, dimensionality reduction), data preprocessing, robust model evaluation, and building scalable, production-ready ML pipelines.

A comprehensive toolkit for building, evaluating, and auditing survival models, specifically handling right-censored and competing-risk outcomes. It supports various models including Cox PH, survival forests, and SVMs, while providing best practices for leakage-safe preprocessing and robust performance evaluation in clinical or scientific ML workflows.

Stable Baselines3 is a PyTorch-based library providing production-ready implementations of core reinforcement learning algorithms, including PPO, SAC, DQN, and TD3. It offers a scikit-learn-like API for easily training RL agents, developing custom Gymnasium environments, and managing complex training workflows. Ideal for standard RL research, rapid prototyping, and advanced model optimization.