Skill UI

Pysam offers Python bindings to htslib for reading and writing SAM/BAM/CRAM alignments, VCF/BCF variants, and FASTA/FASTQ sequences, enabling coverage stats, pileup analysis, variant queries, and seamless integration into NGS pipelines.

QuTiP is a Python toolbox for simulating open and closed quantum systems, offering solvers for master equations, trajectory methods, visualization, and analysis tailored to quantum research, education, and exploratory physics studies.

RDKit is a comprehensive cheminformatics library providing powerful Python APIs for molecular analysis and manipulation. It supports reading/writing diverse molecular formats (SMILES, SDF, InChI), calculating physicochemical descriptors (MW, LogP, TPSA), performing substructure searching, and analyzing stereochemistry. This tool is essential for drug discovery, computational chemistry, and advanced cheminformatics research workflows.

Scanpy is a comprehensive Python toolkit built on AnnData for analyzing single-cell RNA-seq datasets. It provides a complete, scalable workflow covering every stage of single-cell genomics analysis, including quality control (QC), normalization, dimensionality reduction (PCA, UMAP, t-SNE), cell clustering, marker gene identification, and cell type annotation. Ideal for researchers generating publication-quality single-cell visualizations and performing exploratory analysis.

Toolkit for Python-based survival and time-to-event modeling with scikit-survival, supporting Cox models, ensemble learners, SVMs, and evaluation metrics for censored data.

Seaborn is a powerful Python library for creating publication-quality statistical graphics. It is designed for dataset-oriented plotting, making it ideal for exploratory data analysis, multivariate analysis, and visualizing complex relationships, distributions, and comparisons with minimal code. It builds upon Matplotlib, offering high-level interfaces for functions like scatterplots, heatmaps, and pair plots.

A comprehensive guide for systematic statistical analysis, designed for academic research. This skill assists in selecting the correct hypothesis tests (t-test, ANOVA, regression, chi-square), checking assumptions (normality, homogeneity), performing advanced analyses (Bayesian methods, power analysis), calculating effect sizes, and generating full APA-formatted reports for publication.

Statsmodels is a comprehensive Python library dedicated to advanced statistical modeling and econometrics. It provides tools for rigorous statistical inference, including Ordinary Least Squares (OLS), Generalized Linear Models (GLM), mixed-effects models, and time series analysis (ARIMA). Use this skill for detailed diagnostics, coefficient estimation, hypothesis testing, and producing publication-ready statistical reports in fields like econometrics and biostatistics.

This skill connects to the DeepEar Lite platform to fetch real-time, high-frequency financial signals. It retrieves crucial data points including signal titles, sentiment summaries, confidence scores, and detailed reasoning. Use this when you require immediate, actionable insights into volatile market trends, stock performance factors, or comprehensive financial analysis.

This skill utilizes the Kronos model to perform advanced financial time-series forecasting. It generates quantitative base predictions and then integrates news sentiment analysis to adjust and refine the forecast, making it an essential tool for finance professionals needing actionable market insights.

This comprehensive guide provides advanced workflows for interacting with ClickHouse. It covers efficient bulk data insertion (CSV, JSON), complex analytical querying (funnel analysis, retention), implementing materialized views for pre-aggregation, and utilizing powerful window functions. Ideal for building high-performance OLAP dashboards and executing large-scale data transformations.

This comprehensive guide teaches users how to set up a ClickHouse database environment. It covers creating a MergeTree table, inserting structured event data using TypeScript, and executing essential analytical queries (e.g., counting, grouping, unique user metrics). Ideal for beginners learning OLAP databases, testing connections, or mastering core data warehousing concepts.