Agent skill

fluidsim

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.

LeonChaoXgithub.com/LeonChaoXGitHub ↗
claude-codeMIT
Install
npx skills add LeonChaoX/qinyan-academic-skills --skill fluidsim --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 7
SKILL.md size: 9 KB
Bundled scripts: none
Path: skills/10-材料科学与物理计算/fluidsim/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 759
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# FluidSim ## Overview 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**: - Multiple solvers: 2D/3D Navier-Stokes, shallow water, stratified flows - High performance: Pythran/Transonic compilation, MPI parallelization - Complete workflow: Parameter configuration, simulation execution, output analysis - Interactive analysis: Python-based post-processing and visualization ## Core Capabilities ### 1. Installation and Setup Install fluidsim using uv with appropriate feature flags: ```bash # 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): ```bash export FLUIDSIM_PATH=/path/to/simulation/outputs export FLUIDDYN_PATH_SCRATCH=/path/to/working/directory ``` No API keys or authentication required. See `references/installa

What's inside
Steps it walks through
  1. Overview
  2. Core Capabilities
  3. 1. Installation and Setup
  4. 2. Running Simulations
  5. 3. Available Solvers
  6. 4. Parameter Configuration
  7. 5. Output and Analysis
  8. 6. Advanced Features
  9. Common Use Cases
  10. 2D Turbulence Study
  11. Stratified Flow Simulation
  12. High-Resolution 3D Simulation with MPI
  13. Taylor-Green Vortex Validation
  14. Quick Reference
Ships with 6 files
  • references/advanced_features.md
  • references/installation.md
  • references/output_analysis.md
  • references/parameters.md
  • references/simulation_workflow.md
  • references/solvers.md
Commands it runs
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]"
export FLUIDSIM_PATH=/path/to/simulation/outputs
export FLUIDDYN_PATH_SCRATCH=/path/to/working/directory
mpirun -np 8 python simulation_script.py
mpirun -np 64 python script.py
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About this skill
What does the fluidsim skill do?

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.

How do I install it?

Run `npx skills add LeonChaoX/qinyan-academic-skills --skill fluidsim --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From LeonChaoX/qinyan-academic-skills, a repository with 759 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going