LQF_Machine_Learning_E…
LQF Machine Learning Expert Guide - Routed skill for ML/Statistical Modeling with Critical Discussion Mode. Triggers on: machine…
Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides
Claude Code
$ npx -y skills add foryourhealth111-pixel/Vibe-Skills --skill fluidsim --agent claude-code
Codex
$ npx skills add foryourhealth111-pixel/Vibe-Skills --skill fluidsim -a codex -y
Cursor
$ npx skills add foryourhealth111-pixel/Vibe-Skills --skill fluidsim -a cursor -y
Gemini CLI
$ npx skills add foryourhealth111-pixel/Vibe-Skills --skill fluidsim -a gemini-cli -y
GitHub Copilot
$ npx skills add foryourhealth111-pixel/Vibe-Skills --skill fluidsim -a github-copilot -y
OpenCode
$ npx skills add foryourhealth111-pixel/Vibe-Skills --skill fluidsim -a opencode -y
Windsurf
$ npx skills add foryourhealth111-pixel/Vibe-Skills --skill fluidsim -a windsurf -y
name: fluidsim
description: Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.
license: CeCILL FREE SOFTWARE LICENSE AGREEMENT
metadata:
skill-author: K-Dense Inc.Use this skill only for FluidSim or explicit CFD workflows such as Navier-Stokes, turbulence, shallow-water, stratified-flow, or pseudospectral fluid simulations. Do not use it for CSS fluid layouts, responsive design, generic Python simulation, generic PDE solving, or non-FluidSim numerical physics work.
FluidSim is an object-oriented Python framework for high-performance computational fluid dynamics (CFD) simulations. It provides solvers for periodic-domain equations using pseudospectral methods with FFT, delivering performance comparable to Fortran/C++ while maintaining Python's ease of use.
**Key strengths**:
Install fluidsim using uv with appropriate feature flags:
# Basic installation uv uv pip install fluidsim # With FFT support (required for most solvers) uv uv pip install "fluidsim[fft]" # With MPI for parallel computing uv uv pip install "fluidsim[fft,mpi]"
Set environment variables for output directories (optional):
export FLUIDSIM_PATH=/path/to/simulation/outputs export FLUIDDYN_PATH_SCRATCH=/path/to/working/directory
No API keys or authentication required.
See `references/installation.md` for complete installation instructions and environment configuration.
Standard workflow consists of five steps:
**Step 1**: Import solver
from fluidsim.solvers.ns2d.solver import Simul
**Step 2**: Create and configure parameters
params = Simul.create_default_params() params.oper.nx = params.oper.ny = 256 params.oper.Lx = params.oper.Ly = 2 * 3.14159 params.nu_2 = 1e-3 params.time_stepping.t_end = 10.0 params.init_fields.type = "noise"
**Step 3**: Instantiate simulation
sim = Simul(params)
**Step 4**: Execute
sim.time_stepping.start()
**Step 5**: Analyze results
sim.output.phys_fields.plot("vorticity")
sim.output.spatial_means.plot()See `references/simulation_workflow.md` for complete examples, restarting simulations, and cluster deployment.
Choose solver based on physical problem:
**2D Navier-Stokes** (`ns2d`): 2D turbulence, vortex dynamics
from fluidsim.solvers.ns2d.solver import Simul
**3D Navier-Stokes** (`ns3d`): 3D turbulence, realistic flows
from fluidsim.solvers.ns3d.solver import Simul
**Stratified flows** (`ns2d.strat`, `ns3d.strat`): Oceanic/atmospheric flows
from fluidsim.solvers.ns2d.strat.solver import Simul params.N = 1.0 # Brunt-Väisälä frequency
**Shallow water** (`sw1l`): Geophysical flows, rotating systems
from fluidsim.solvers.sw1l.solver import Simul params.f = 1.0 # Coriolis parameter
See `references/solvers.md` for complete solver list and selection guidance.
Parameters are organized hierarchically and accessed via dot notation:
**Domain and resolution**:
params.oper.nx = 256 # grid points params.oper.Lx = 2 * pi # domain size
**Physical parameters**:
params.nu_2 = 1e-3 # viscosity params.nu_4 = 0 # hyperviscosity (optional)
**Time stepping**:
params.time_stepping.t_end = 10.0 params.time_stepping.USE_CFL = True # adaptive time step params.time_stepping.CFL = 0.5
**Initial conditions**:
params.init_fields.type = "noise" # or "dipole", "vortex", "from_file", "in_script"
**Output settings**:
params.output.periods_save.phys_fields = 1.0 # save every 1.0 time units params.output.periods_save.spectra = 0.5 params.output.periods_save.spatial_means = 0.1
The Parameters object raises `AttributeError` for typos, preventing silent configuration errors.
See `references/parameters.md` for comprehensive parameter documentation.
FluidSim produces multiple output types automatically saved during simulation:
**Physical fields**: Velocity, vorticity in HDF5 format
sim.output.phys_fields.plot("vorticity")
sim.output.phys_fields.plot("vx")**Spatial means**: Time series of volume-averaged quantities
sim.output.spatial_means.plot()
**Spectra**: Energy and enstrophy spectra
sim.output.spectra.plot1d() sim.output.spectra.plot2d()
**Load previous simulations**:
from fluidsim import load_sim_for_plot
sim = load_sim_for_plot("simulation_dir")
sim.output.phys_fields.plot()**Advanced visualization**: Open `.h5` files in ParaView or VisIt for 3D visualization.
See `references/output_analysis.md` for detailed analysis workflows, parametric study analysis, and data export.
**Custom forcing**: Maintain turbulence or drive specific dynamics
params.forcing.enable = True params.forcing.type = "tcrandom" # time-correlated random forcing params.forcing.forcing_rate = 1.0
**Custom initial conditions**: Define fields in script
params.init_fields.type = "in_script"
sim = Simul(params)
X, Y = sim.oper.get_XY_loc()
vx = sim.state.state_phys.get_var("vx")
vx[:] = sin(X) * cos(Y)
sim.time_stepping.start()**MPI parallelization**: Run on multiple processor
Intelligent Skill routing and workflow orchestration for AI agents — +21.12 pp reward, −29.6% tokens on SkillsBench with DeepSeekV4Flash-VE.
LQF Machine Learning Expert Guide - Routed skill for ML/Statistical Modeling with Critical Discussion Mode. Triggers on: machine…
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental…
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting,…
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request…
Access real-time and historical stock market data, forex rates, cryptocurrency prices, commodities, economic indicators, and 50+…
Implement proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design.…
Direct browser control via CDP for web interaction: automation, scraping, testing, screenshots, and site/app work. Also covers…
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling…