Skill UI

This comprehensive toolkit facilitates the entire phylogenetic analysis workflow, from sequence alignment to tree inference. It integrates industry-standard tools like MAFFT for multiple sequence alignment, TrimAl for alignment trimming, and powerful maximum likelihood methods using IQ-TREE 2 or FastTree. Ideal for molecular genomics, tracing viral phylodynamics, studying protein evolution, or conducting molecular clock analysis. Built using Python for seamless integration.

Polars is a lightning-fast DataFrame library built for Python and Rust, leveraging Apache Arrow. It provides an expression-based API and lazy evaluation framework for highly efficient data manipulation. It is ideal for complex data pipelines, large-scale ETL tasks, and migrating from Pandas due to its superior performance, parallel execution capabilities, and optimized query planning.

PrimeKG is a comprehensive knowledge graph integrating over 20 primary databases to map complex biological relationships. It allows users to query multiscale data, including genes, drugs, diseases, and phenotypes. Ideal for drug discovery, identifying repurposing opportunities, and analyzing deep disease contexts in network pharmacology.

A specialized tool for safely reading, validating, and exporting protocols.io data using current, official REST/MCP contracts. It provides a read-only, planned workflow to ensure data integrity, supporting complex scientific data extraction and workflow automation while strictly adhering to API governance and security best practices.

PyDESeq2 is a robust Python implementation of DESeq2, designed for comprehensive differential expression analysis using bulk RNA-seq count data. It supports complex formulaic designs, performs Wald tests with multiple testing correction (FDR), and includes advanced features like LFC shrinkage, enabling researchers to analyze gene expression changes between various experimental conditions within Python pipelines.

A powerful Python library for analyzing, validating, converting, and transforming complex materials structures and associated computational data. It provides functionalities for handling compositions, periodic crystals, molecules, local phase diagrams, symmetry assignments, and electronic structure I/O, ensuring strict provenance tracking for all transformations.

PyMC is a powerful Python library designed for probabilistic programming and sophisticated Bayesian modeling. It enables users to build complex models, such as hierarchical structures and time series, and perform rigorous inference using methods like MCMC (NUTS) sampling and Variational Inference. Key features include model comparison (LOO, WAIC) and comprehensive diagnostic tools for uncertainty quantification.

Pymoo is a comprehensive Python library designed for tackling complex optimization problems, especially those involving multiple conflicting objectives. It implements state-of-the-art evolutionary algorithms (like NSGA-II, MOEA/D) to efficiently locate the Pareto front. It is invaluable for engineering design, scientific modeling, and decision-making processes requiring the analysis of trade-off solutions.

PySam provides a powerful Python interface for low-level, streaming access to HTSlib-supported genomic data formats. It enables reading, querying, filtering, and writing complex formats such as SAM/BAM/CRAM, VCF/BCF, FASTA/FASTQ, and tabix-indexed tables. It is essential for bioinformatics pipelines requiring precise control over genomic data manipulation, including pileup and coverage calculations.

Compiles comprehensive, verifiable external evidence for writing high-quality scientific manuscripts or research briefs. It conducts deep, multi-source academic searches, targeting verified references and structuring the findings into a manuscript-ready research packet. Ideal for in-depth literature reviews, hypothesis testing, and building structured evidence matrices.

A highly structured framework designed for generating, organizing, and transparently prioritizing scientific research concepts. It enforces independent idea generation, explicit assumption labeling, structured discussion, and evidence-aware evaluation, ensuring the creation of robust, proposal-level research directions for early-stage exploration.

This skill guides the creation and auditing of truthful, accessible, and publication-ready scientific figures. It emphasizes maintaining data integrity by separating universal scientific principles from journal-specific rules. Users learn proper encoding (e.g., using position on common scales, handling uncertainty/missing data) and implementing designs with scope-aware Python libraries like Matplotlib and Seaborn, ensuring compliance with WCAG standards and maximizing scientific rigor.