Skill UI

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.

This skill compiles comprehensive external evidence required for drafting high-quality scientific manuscripts or research briefs. It goes beyond simple link lists, targeting verified, unique academic references (e.g., 60 references) and generating a structured research packet. Use it when you need literature reviews, evidence supporting a claim, or a deep dive into scientific background, methodology, or limitations.

A comprehensive, scalable Python toolkit designed for end-to-end single-cell RNA-seq data analysis. It supports crucial steps including quality control (QC), normalization, dimensionality reduction (PCA, UMAP, t-SNE), cell clustering, and differential gene expression analysis. The skill provides ready-to-run scripts for analyzing various formats (e.g., .h5ad, 10X, R-native) and generating publication-quality visualizations and cell type annotations.

This skill provides a systematic framework for evaluating the rigor of scientific claims, research methodology, and evidence quality. It guides users through assessing experimental design validity, identifying various types of biases (e.g., selection, cognitive), and applying established grading frameworks like GRADE or Cochrane Risk of Bias. Ideal for systematic reviews, peer review, and critical analysis of scientific literature.

A meta-skill dedicated to creating high-quality, publication-ready scientific figures. It guides users on best practices for journal submission, including multi-panel layouts, precise error bar inclusion, colorblind-safe palette usage, and adherence to specific journal styles (Nature, Science). It orchestrates libraries like matplotlib, seaborn, and plotly to ensure optimal resolution and format export (PDF, EPS, TIFF).

scikit-bio is a comprehensive Python library designed for advanced bioinformatics and biological data analysis. It provides robust capabilities for sequence manipulation (DNA, RNA, Protein), performing complex sequence alignments (pairwise and multiple), constructing and analyzing phylogenetic trees, and calculating diverse ecological metrics. It is essential for microbiome analysis, evolutionary studies, and general biological data processing, supporting standard formats like FASTA, Newick, and BIOM.

A comprehensive resource detailing the use of scikit-learn, the industry-standard Python library for classical machine learning. Use this skill for the entire ML lifecycle, including supervised tasks (classification, regression), unsupervised tasks (clustering, dimensionality reduction), data preprocessing, model evaluation, and building production-ready pipelines.

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.

This skill guides users through the entire statistical analysis workflow, covering everything from framing research questions and checking assumptions to running complex models (t-tests, ANOVA, regression, Bayesian methods). It emphasizes calculating effect sizes and generating fully formatted results compatible with APA guidelines, ensuring rigorous and reproducible research reporting.

SymPy is a powerful Python library designed for exact symbolic computation, allowing users to perform advanced mathematical operations without resorting to floating-point approximations. It provides comprehensive tools for symbolic algebra, differentiation, integration, solving complex equations (linear and non-linear), and handling linear algebra. It is essential for scientific computing, physics modeling, and advanced mathematical research, supporting multiple output formats like LaTeX.

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.