Skill UI

QuTiP is a powerful Python library designed for simulating and analyzing quantum mechanical systems. It provides comprehensive tools to model both closed (unitary) and open (dissipative) quantum dynamics. Users can study complex phenomena like decoherence, quantum optics, and master equations. The library supports various time evolution solvers, calculating entanglement, entropy, and visualizing quantum states on the Bloch sphere or in phase space. Ideal for theoretical physics research and academic simulations.

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.

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.

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.

TorchDrug is a comprehensive PyTorch-based ML toolbox designed for molecular and protein science. It leverages Graph Neural Networks (GNNs) to tackle complex tasks including molecular property prediction, protein structure modeling, drug-target binding prediction, knowledge graph reasoning, and de novo molecular generation. It provides robust capabilities for accelerating drug discovery research.

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.

This advanced technique allows users to identify an obscure actor by linking indirect clues across three domains: the director, the film's characteristics, and the actor's biographical details. It is essential when direct searches fail, using the director's work or a film's context as the primary entry point to resolve the actor's identity.

A comprehensive skill designed for finance-related information retrieval. It provides dual search capabilities: real-time web searching across multiple engines (Jina, DDG, Baidu) for general market knowledge, and Retrieval-Augmented Generation (RAG) for querying local, proprietary document stores (e.g., daily news). Ideal for financial analysts needing both broad market context and specific internal data insights.

This skill enables searching for stock tickers across A-Share, HK, and US markets. It can retrieve comprehensive historical price data (OHLCV) over specified date ranges, and also fetches crucial fundamental financial metrics such as market capitalization, P/E ratio, and industry sector information. Use this tool when needing to analyze the past or current performance of a publicly traded company.