Skill UI

Create professional, data-driven infographics effortlessly. This skill uses advanced AI (Nano Banana Pro) integrated with web research to gather accurate statistics and facts. It supports various visualization types (e.g., timelines, comparisons, statistical charts) and includes a smart iterative review process, ensuring the output meets high quality standards for specific document types like marketing materials or business reports. No design expertise required.

This comprehensive guide teaches how to create professional, publication-quality research posters using LaTeX packages (beamerposter, tikzposter). It covers best practices for academic layout, typography, and color schemes. Crucially, it provides strict guidelines for AI-powered visual element generation, emphasizing simplicity, high contrast, and minimal content density for optimal poster readability and visual impact.

Matchms is an open-source Python library designed for comprehensive mass spectrometry data processing. It handles spectrum comparison, calculates various similarity scores (like cosine and modified cosine), and aids in identifying unknown compounds. It is highly valuable for metabolomics and spectral library searching pipelines.

Matplotlib is Python's foundational library for creating highly customizable static, animated, and interactive visualizations. It supports various plot types (line, scatter, heatmap, 3D) and offers both pyplot and object-oriented interfaces. Ideal for generating publication-quality scientific charts, complex multi-panel figures, and integrating into notebooks.

Modal is a powerful serverless cloud platform designed specifically for running AI and Machine Learning workloads. It allows users to deploy, serve, and scale complex models (like LLMs) without managing infrastructure. Key features include on-demand access to high-end GPUs (A100, H100), serverless function scaling, persistent storage, and the ability to define entire infrastructure using pure Python code, eliminating the need for separate YAML or Dockerfiles.

Perform comprehensive molecular dynamics (MD) simulations using high-performance engines like OpenMM and analysis tools like MDAnalysis. This skill allows users to model the time evolution of biological systems, covering system preparation (solvation, force field assignment), energy minimization, NVT equilibration, and advanced analysis (RMSD, free energy surfaces). Ideal for structural biology, drug binding studies, and biophysics research.

An end-to-end toolkit designed for analyzing high-density Neuropixels electrophysiology recordings. It implements best practices using SpikeInterface, covering the entire workflow: data loading (SpikeGLX, NWB), preprocessing, motion/drift correction, advanced spike sorting (Kilosort4), quality metric computation, and unit curation (including AI-assisted review). Ideal for neuroscience research.

This skill provides a comprehensive Python API integration for the OMERO platform, an open-source solution for managing and analyzing large-scale microscopy datasets. Users can programmatically access images, retrieve structured data, analyze raw pixel data, manage complex annotations (ROIs), and execute batch processing workflows essential for high-content screening and advanced biological research.

An expert skill for transforming CPU-bound Python code into highly performant GPU-accelerated code using NVIDIA technologies. It specializes in optimizing scientific workflows, including machine learning (ML), physics simulations, geospatial analysis, and large-scale array computations. Utilize tools like CuPy for array operations, Numba CUDA for custom kernels, and Warp for spatial computing and differentiable simulations, achieving dramatic speedups (10x to 1000x). Use this when dealing with NumPy, pandas, SciPy, or complex computational loops that benefit from parallel processing on NVI...

Polars is a lightning-fast DataFrame library built on Apache Arrow for Python. It provides an expression-based API, lazy query optimization, and parallel computation capabilities for efficient data manipulation, analytics, and ETL pipelines. It is designed to handle large, memory-intensive datasets, making it an ideal alternative for migrating from pandas or optimizing complex data workflows.

A high-performance library built on Polars for advanced genomic interval operations and bioinformatics file I/O. It provides robust support for core interval arithmetic (overlap, merge, complement) and reading/writing major formats like VCF, BAM, BED, and GFF. Features include out-of-core streaming, cloud-native I/O (S3, GCS, Azure), and a powerful SQL interface, making it ideal for large-scale genomic research.

PufferLib is a high-performance, scalable RL framework designed for massive parallel training and vectorized simulation. Featuring PuffeRL (optimized PPO+LSTM) and the PufferEnv API, it supports multi-agent systems, custom environment creation, and achieves industry-leading speeds (up to millions of steps/second). Ideal for advanced research and large-scale AI experimentation.