Skill UI

Pymatgen is a comprehensive Python toolkit designed for computational materials science. It enables users to create, analyze, and manipulate complex crystal structures, calculate electronic properties (DOS, band structures), simulate phase transitions, and interact with large material databases like the Materials Project. Ideal for solid-state physics, chemistry, and advanced materials research.

A comprehensive Python wrapper for OpenMS, designed for sophisticated computational mass spectrometry workflows. It supports end-to-end analysis in proteomics and metabolomics, enabling feature detection, peptide/protein identification, quantitative analysis (label-free/isobaric), and adduct/accurate mass annotation. Ideal for researchers processing complex LC-MS/MS raw data and running high-level pipelines.

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 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.

This skill provides comprehensive guidance and functionality for creating professional, visually stunning, and academically rigorous slide decks. Designed for research talks, conference presentations, and thesis defenses, it assists in structuring the narrative, integrating complex data visualizations, maintaining consistent design aesthetics, and including proper citations. The recommended output is a combined PDF for maximum visual impact.

This core skill is designed for generating complete, publication-ready scientific manuscripts. It guides users through a rigorous two-stage process: first, creating structured outlines using deep research lookup, and second, converting these into flowing, authoritative prose. It supports IMRAD structure, advanced citation formatting (APA, AMA, Vancouver), and mandatorily integrates complex visual elements like graphical abstracts and technical schematics, ensuring the final output meets high journal submission standards.

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.

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.

This skill automates the process of transforming high-level product specifications, requirements, and design documents into a detailed, actionable, and executable task plan. It follows a multi-stage workflow, including context loading, parallel research, and two rigorous review gates (Task Plan Review and Task-Graph Sanity Review). The output provides properly sized (1-3 hours), logically sequenced tasks, ensuring every requirement is covered and dependencies are explicitly managed, dramatically improving project planning accuracy.

A comprehensive coach designed to guide students and researchers through the entire scholarly writing lifecycle. This skill covers structuring complex academic works—including research papers (IMRaD), theses, dissertations, grant proposals, and conference abstracts. It teaches fundamental writing principles, proper source integration, avoiding plagiarism, and navigating diverse citation styles (APA, MLA, Chicago, GB/T 7714), helping users develop an independent, professional academic voice.