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.
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionfluidsimExecute the skills CLI command in your project's root directory to begin installation:
Fetches fluidsim from davila7/claude-code-templates and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate fluidsim. Access via /fluidsim in your agent's command palette.
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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 processors
mpirun -np 8 python simulation_script.py
Parametric studies: Run multiple simulations with different parameters
for nu in [1e-3, 5e-4, 1e-4]:
params = Simul.create_default_params()
params.nu_2 = nu
params.output.sub_directory = f"nu{nu}"
sim = Simul(params)
sim.time_stepping.start()
See references/advanced_features.md for forcing types, custom solvers, cluster submission, and performance optimization.
from fluidsim.solvers.ns2d.solver import Simul
from math import pi
params = Simul.create_default_params()
params.oper.nx = params.oper.ny = 512
params.oper.Lx = params.oper.Ly = 2 * pi
params.nu_2 = 1e-4
params.time_stepping.t_end = 50.0
params.time_stepping.USE_CFL = True
params.init_fields.type = "noise"
params.output.periods_save.phys_fields = 5.0
params.output.periods_save.spectra = 1.0
sim = Simul(params)
sim.time_stepping.start()
# Analyze energy cascade
sim.output.spectra.plot1d(tmin=30.0Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Steps
- 1Install skill using provided installation command
- 2Test with simple use case relevant to your work
- 3Evaluate output quality and relevance
- 4Iterate on prompts to improve results
- 5Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
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Frontendsame categoryReviews
4.8★★★★★32 reviews- PPratham Ware★★★★★Dec 12, 2024
Keeps context tight: fluidsim is the kind of skill you can hand to a new teammate without a long onboarding doc.
- AAdvait Yang★★★★★Dec 12, 2024
fluidsim fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- NNeel Martinez★★★★★Dec 8, 2024
fluidsim is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- NNoor Singh★★★★★Nov 27, 2024
fluidsim reduced setup friction for our internal harness; good balance of opinion and flexibility.
- AAditi Ramirez★★★★★Nov 15, 2024
Keeps context tight: fluidsim is the kind of skill you can hand to a new teammate without a long onboarding doc.
- SSakshi Patil★★★★★Nov 3, 2024
Registry listing for fluidsim matched our evaluation — installs cleanly and behaves as described in the markdown.
- CChaitanya Patil★★★★★Oct 22, 2024
fluidsim reduced setup friction for our internal harness; good balance of opinion and flexibility.
- NNoor Jain★★★★★Oct 18, 2024
Registry listing for fluidsim matched our evaluation — installs cleanly and behaves as described in the markdown.
- IIsabella Malhotra★★★★★Oct 6, 2024
fluidsim is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- AAnaya Gill★★★★★Sep 17, 2024
I recommend fluidsim for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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