Skill UI

Use UMAP for fast nonlinear manifold reduction to produce embeddings that keep local and global structure, making it ideal for visualization, clustering preprocessing, supervised/parametric workflows, or high-dimensional feature engineering.

Direct REST API access to UniProt for searching proteins, fetching FASTA sequences, mapping identifiers, selecting annotations, and streaming large datasets within Python workflows.

GeoMaster is a comprehensive geospatial science skill that guides remote sensing, GIS, spatial analysis, and machine learning workflows for Earth observation and planetary data projects, covering satellite imagery, vector/raster math, point clouds, terrain, hydrology, and cloud-native data stacks.

Query InterPro for protein family, domain, and GO annotations via a REST API with Python helpers, enabling function prediction, domain architecture mapping, and evolutionary classification insights for proteomic workflows.

Run OpenMM-powered molecular dynamics simulations and MDAnalysis trajectory workflows to build protein or small-molecule systems, minimize energy, equilibrate conditions, and characterize RMSD/RMSF, contacts, and free-energy surfaces for structural biology or drug studies.

Provide a comprehensive toolkit for RNA velocity inference in single-cell RNA-seq data, modeling unspliced/spliced kinetics to infer differentiation directions, latent time, and driver genes while complementing Scanpy pipelines.

A comprehensive toolkit for reconstructing and analyzing the evolutionary history of biological sequences (genes, proteins, genomes). The standard workflow includes multiple sequence alignment using MAFFT, robust maximum likelihood tree inference with IQ-TREE 2 and FastTree, and advanced visualization/manipulation using ETE3. This skill is essential for studies in viral phylodynamics, microbial genomics, protein family evolution, and molecular clock dating.

Literature Review Tool helps researchers gather and synthesize key insights from scholarly texts, highlighting patterns and critical arguments to accelerate literature surveying workflows even when structured content is initially unavailable.

Pandas is a Python data analysis library that simplifies loading, cleaning, transforming, and analyzing structured datasets, making it ideal for data wrangling in analytics or engineering workflows.

PyMC is a powerful Python library for probabilistic programming and advanced Bayesian inference. This comprehensive guide details the full workflow for building, fitting, and validating complex Bayesian models. Learn to implement hierarchical structures, perform MCMC sampling (NUTS), conduct posterior predictive checks (PPC), and effectively quantify uncertainty using industry best practices.

Placeholder skill entry for risk modeling workflows that outlines basic analytics and scenario planning used to quantify potential losses and guide mitigation decisions.

A comprehensive skill for ingesting, analyzing, and manipulating multi-modal video and audio content. Supports real-time desktop capture, live stream monitoring (RTSP), and file uploads (URL/local). Capabilities include advanced indexing (visual, semantic, temporal), precise moment searching, professional timeline editing (subtitles, overlays, dubbing), transcoding, and generating actionable alerts. Ideal for content creators, monitoring systems, and complex media workflows.