Skill UI

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.

QuTiP 5 is a comprehensive library for simulating and auditing complex quantum-system models, including closed and open quantum dynamics. It supports various physical models such as Lindblad dynamics, quantum trajectories, Bloch-Redfield models, and specialized methods like HEOM and Floquet. This tool is essential for local quantum-dynamics research requiring explicit control over physical assumptions, dimensions, and numerical convergence.

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.

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.

scvi-tools is a comprehensive Python framework utilizing deep generative models and variational inference for analyzing diverse single-cell data modalities. It excels in complex analyses such as probabilistic batch correction (scVI), multi-modal integration (MultiVI), and differential expression analysis with uncertainty. It supports single-cell RNA-seq, ATAC-seq, and various multi-omics datasets, providing advanced tools for modern genomic research.

SimPy is a powerful Python library designed for building, inspecting, testing, and analyzing bounded process-based discrete-event simulations. It supports modeling complex systems like queues, logistics networks, and service operations by providing an event scheduler and modeling primitives. It is essential for academic and industrial applications requiring robust system analysis.

PyG is the standard, powerful library for building Graph Neural Networks (GNNs) atop PyTorch. It provides specialized data structures for nodes, edges, and graphs, supporting various tasks like node classification, graph classification, and link prediction. Users can implement advanced models using built-in layers (GCN, GAT, GraphSAGE) and handle complex structures like heterogeneous graphs efficiently. It is essential for deep learning research and industrial applications involving graph data.

UMAP (Uniform Manifold Approximation and Projection) is a powerful technique for non-linear dimensionality reduction. This skill enables generating scalable and structure-preserving embeddings for visualization (2D/3D), clustering preprocessing, and feature engineering. It supports supervised and semi-supervised workflows, allowing users to guide the embedding process with labeled data while maintaining local and global data structure integrity.

Zarr is a robust Python library designed for efficiently storing and managing large N-dimensional arrays. It provides features like chunking, compression, and parallel I/O, making it perfectly suited for cloud-native scientific computing pipelines. It ensures seamless integration with major scientific stacks including NumPy, Dask, and Xarray, while supporting remote storage backends like S3 and GCS.

This tool automates the complete specification generation process across four core phases: Initialization, Requirements, Design, and Tasks. It supports both interactive (step-by-step approval) and automatic execution modes. After generating the full spec, it performs a final sanity review to ensure coherence and completeness, providing a ready-to-implement blueprint.

This utility script is designed to collect comprehensive diagnostic evidence for Adobe API integrations. It gathers crucial details such as installed SDK versions, credential validation status, API endpoint connectivity checks, and environment variables, all packaged into a single, redacted archive for efficient troubleshooting and support ticket submission.