Skill UI

This comprehensive guide details security-first development practices, emphasizing threat modeling (STRIDE) and secure design principles. It provides mandatory controls for validating all external input, preventing common vulnerabilities like XSS and SQL injection, and properly managing authentication and sensitive data storage. Use it when building any feature that handles untrusted data or interacts with external systems.

Guides an AI pair to document application needs, access patterns, and scaling concerns while producing Azure Cosmos DB NoSQL requirements and data model artifacts using established best practices in a structured workflow.

Structured pipeline for credit risk pre-loan modeling that loads and formats raw data, filters abnormal periods, trims high-missing/low-IV/high-PSI/high-correlation features, denoises via Null Importance, and exports an Excel cleaning report for review.

Guides creating, altering, and validating Snowflake semantic views via the Snowflake CLI, covering connection setup, star-schema modeling, metadata comments/synonyms, temporary validation steps, and post-validation querying for reliable semantic layer delivery.

Hypogenic is an advanced framework that uses Large Language Models (LLMs) to systematically generate and test hypotheses on complex tabular datasets. It supports three powerful modes: data-driven generation (HypoGeniC), literature-grounded refinement (HypoRefine), and combined union methods. This tool is essential for accelerating scientific discovery in fields such as predictive modeling, content analysis, and deception detection by combining empirical data insights with theoretical knowledge.

A comprehensive guide to MATLAB and GNU Octave, covering core scientific computing tasks. This skill details capabilities in matrix operations, linear algebra, data visualization (2D/3D), statistical analysis, signal processing, and solving differential equations. It is ideal for users requiring complex numerical simulation, data modeling, and scientific scripting.

scikit-survival is a comprehensive Python library built on scikit-learn, designed for rigorous time-to-event analysis. It specializes in handling censored data (right, left, or interval-censored) and provides multiple advanced modeling techniques, including Cox proportional hazards models, Random Survival Forests, Gradient Boosting, and Survival SVMs. Users can evaluate model performance using metrics like the Concordance Index and Brier Score, making it ideal for clinical, reliability, and survival research.

scVelo is a powerful Python package designed for RNA velocity analysis using single-cell RNA-seq data. It estimates cell state transitions by modeling the kinetics of mRNA splicing—specifically using the ratio of unspliced to spliced mRNA abundances. This allows researchers to reconstruct developmental trajectories and predict cell fates, even when only snapshot data is available, making it a vital complement to standard trajectory inference tools like Scanpy.

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.

A comprehensive Python wrapper for the U.S. Treasury Fiscal Data REST API. This tool allows users to query diverse federal financial datasets, including national debt, interest rates, foreign exchange rates, and government revenue/spending statistics. It requires no API key and is ideal for financial modeling, economic research, and data analytics projects.

A comprehensive tutor designed to build robust statistical thinking. It guides users through the entire data analysis lifecycle, from exploratory data visualization and descriptive statistics to complex hypothesis testing and regression modeling. The focus is on understanding the 'why' and 'how' of statistical logic using real datasets and practical problems, rather than just memorizing formulas. It is tool-agnostic, supporting concepts learned in Python, R, SPSS, or Stata.

Generates high-quality, deterministic, and editable vector diagrams (SVG) for system architecture, workflows, and pipelines. By using a structured JSON specification (FigureSpec), it ensures that the same input always yields the identical output, making it ideal for reproducible technical documentation, academic papers, and formal system modeling where precise layout and connections are critical. It is optimized for non-data-plot, formal diagrams.