Skill UI

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.

A meta-skill for generating high-quality, publication-ready scientific figures. It guides users through creating complex visualizations, including multi-panel layouts, error bars, significance annotations, and colorblind-safe palettes. It ensures strict adherence to specific journal guidelines (Nature, Science) regarding resolution, file formats (PDF, EPS, TIFF), and typography.

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 is a powerful Python library built on scikit-learn for conducting advanced survival analysis and time-to-event modeling. It excels at handling censored data (e.g., right-censored) and allows users to fit various models, including Cox proportional hazards, Random Survival Forests, and Gradient Boosting. Users can also evaluate model performance using metrics like the Concordance Index (C-index) and Brier score, making it ideal for biomedical, clinical, or reliability engineering applications.

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.

Seaborn is a powerful Python library designed for creating high-level, publication-quality statistical graphics. It integrates seamlessly with pandas DataFrames, allowing users to easily visualize complex statistical relationships, distributions, and comparisons (e.g., box plots, violin plots, heatmaps). It simplifies multivariate analysis and exploratory data analysis (EDA) with minimal code.

SHAP (SHapley Additive exPlanations) provides a unified framework based on Shapley values to interpret complex machine learning models. It calculates feature attributions, enabling users to understand exactly why a model made a specific prediction. This skill supports generating various visualizations (e.g., waterfall, beeswarm, force plots) for feature importance, model debugging, bias analysis, and implementing robust explainable AI across diverse model types like XGBoost, PyTorch, and TensorFlow.

A comprehensive skill for conducting systematic statistical hypothesis testing. It guides users through the entire data analysis lifecycle, including test selection (t-test, ANOVA, correlation, regression), rigorous assumption checking (normality, homogeneity), running advanced models (Bayesian, frequentist), calculating effect sizes, and generating publication-ready, APA-formatted reports. Ideal for academic research and quantitative analysis.

Statsmodels is a robust Python library designed for comprehensive statistical modeling and econometric analysis. It provides tools for classical linear regression (OLS), generalized linear models (GLM like Poisson and Logistic), time series forecasting (ARIMA), and mixed-effects models. Use this skill for rigorous statistical inference, detailed diagnostic testing, and generating publication-ready statistical tables.

SymPy is a powerful Python library designed for symbolic mathematics. Unlike numerical libraries, it allows for exact computation using mathematical symbols, enabling precise solutions for algebra, calculus (derivatives, integrals), linear algebra, and solving complex equations. This tool is ideal for academic research, physics modeling, and advanced scientific computing requiring non-approximate results. It also supports code generation in various languages.

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.