Skill UI

FluidSim is a Python-based CFD framework offering pseudospectral solvers (2D/3D Navier-Stokes, shallow water, stratified flows) with FFT, MPI, Pythran/Transonic acceleration, and rich output analysis for turbulence or geophysical studies.

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.

Plans, configures, inspects, restarts, and analyzes bounded FluidSim computational-fluid-dynamics simulations with numerical-validity and HPC safety checks. Covers solver selection, parameter review, FFT/MPI setup, output diagnostics, and restart compatibility.

This skill enables the construction and analysis of phylogenetic trees using tools like MAFFT for alignment, IQ-TREE 2 for maximum likelihood inference, and FastTree for large datasets. It supports evolutionary analysis, microbial genomics, viral phylodynamics, and molecular clock studies. Visualization is handled via ETE3 or FigTree. Ideal for inferring evolutionary relationships, detecting horizontal gene transfer, and reconstructing ancestral sequences in biological research.

A specialized skill for achieving asymptotic performance gains when classical algorithms are already near optimal. It leverages advanced mathematical techniques and probabilistic data structures (e.g., Bloom Filters, HyperLogLog, FFT, MinHash/LSH) to handle massive datasets (n ≥ 10⁶) by accepting bounded approximations or exploiting deep structural properties. Requires strict adherence to error parameter and complexity justification protocols.

A specialized skill for achieving asymptotic performance gains when classical algorithms are already near optimal. It leverages advanced mathematical techniques and probabilistic data structures (e.g., Bloom Filters, HyperLogLog, FFT, MinHash/LSH) to handle massive datasets (n ≥ 10⁶) by accepting bounded approximations or exploiting deep structural properties. Requires strict adherence to error parameter and complexity justification protocols.

This skill enables building large procedural oceans and coast transitions in Three.js using spectral methods. It supports WebGPU and TSL FFT oceans, multi-cascade wavelength bands, and coastal breakers. Features include finite-volume shallow-water waves, signed-distance coastlines, wet-sand transitions, and advanced underwater optics like Snell-window views and caustic god rays. Ideal for realistic aquatic environments with GPU validation.

This skill enables building production-ready analytic and bounded water systems in Three.js. It covers photorealistic shallow water simulation using gravity-capillary FFT waves, dispersive caustics, ray-traced volume optics, and Beer-Lambert absorption. Ideal for creating realistic pool, ocean, and water surface effects with accurate normals, refraction, and foam generation.