Skill UI

A comprehensive automation toolkit designed to manage the entire Vercel deployment lifecycle and associated infrastructure. Users can programmatically monitor, trigger, and manage deployments, configure custom domains and DNS records, manage environment variables across multiple targets, and oversee project settings and project listings. Essential for implementing robust CI/CD pipelines and managing cloud infrastructure.

This technique helps users solve highly complex movie trivia or cinematic research questions that involve multiple, intertwined constraints (such as specific awards, historical eras, directors, or plot elements). Instead of attempting to input all variables at once, it teaches the methodology of decomposing the main question into 2-3 independent, searchable dimensions, ensuring a systematic and accurate search process.

COBRApy is a powerful Python library for Constraint-Based Reconstruction and Analysis (COBRA). It enables deep dives into cellular metabolism, allowing users to build, simulate, and analyze complex metabolic models (e.g., genome-scale models). Core functionalities include Flux Balance Analysis (FBA), Flux Variability Analysis (FVA), performing gene/reaction knockouts, and optimizing growth media. It supports standard metabolic model formats like SBML, JSON, and YAML.

This system simulates a multi-disciplinary council, drawing from diverse cognitive archetypes (e.g., Ethicist, Futurist, Contrarian). Instead of a single answer, it generates genuine cognitive diversity by forcing different viewpoints to clash, providing deep, actionable insights for complex decisions, dilemmas, or strategy planning.

Geniml is a comprehensive Python package designed for applying machine learning models to genomic interval data, typically stored in BED files. It provides several unsupervised methods—including Region2Vec and BEDspace—to learn robust embeddings of genomic regions, single cells (scATAC-seq), and associated metadata. Use cases include dimensionality reduction, structural variation analysis, cell-type annotation, and cross-modal querying in genomics research.

A versatile, hardware-agnostic quantum computing framework designed for training quantum circuits and building hybrid quantum-classical models. It provides automatic differentiation, supporting advanced applications in Quantum Machine Learning, Quantum Chemistry (VQE), and variational algorithms, while ensuring portability across major quantum hardware backends (IBM, Google, etc.).

PyDESeq2 is a robust Python implementation for conducting differential gene expression analysis on bulk RNA-seq count data. It supports complete statistical workflows, including data loading from various formats, complex multi-factor design specifications (e.g., accounting for batch effects or covariates), normalization, and performing advanced Wald tests. It is ideal for researchers transitioning R-based DESeq2 analyses into scalable Python pipelines.

PyMC is a comprehensive Python library for probabilistic programming and Bayesian modeling. It enables users to build, fit, and validate complex statistical models, including hierarchical structures, time series, and regression models. It provides powerful tools for MCMC sampling (NUTS) and variational inference, making it essential for rigorous uncertainty quantification and model comparison (LOO/WAIC).

Pysam is a comprehensive Python toolkit for handling genomic data. It provides a Pythonic interface for reading, writing, and manipulating essential genomic formats, including SAM/BAM/CRAM alignments, VCF/BCF variants, and FASTA/FASTQ sequences. It is critical for developing robust bioinformatics pipelines, performing coverage analysis, and analyzing genetic variations.

Qiskit is a leading open-source framework for quantum computing. It enables users to build, optimize, and execute complex quantum circuits and algorithms. The platform supports running on various hardware backends, including IBM Quantum, IonQ, and local simulators. Ideal for advanced research in quantum chemistry simulations, variational quantum algorithms (VQE/QAOA), and quantum machine learning.

A comprehensive Python framework utilizing deep generative models and variational inference for advanced single-cell genomics analysis. It supports the integration and analysis of diverse multi-modal data types, including single-cell RNA-seq, ATAC-seq, CITE-seq, and spatial transcriptomics. Key applications include probabilistic batch correction, cell type annotation, and multi-omics integration.

Seaborn is a high-level Python data visualization library built on Matplotlib, designed for creating aesthetically pleasing and statistically rich graphs. It excels in exploring multivariate relationships, visualizing distributions (like box and violin plots), and handling complex dataset-oriented statistical graphics with minimal code. Ideal for data exploration and generating publication-quality figures.