Agent skill

mesh-generation

Plan and evaluate mesh generation for numerical simulations. Use when choosing grid resolution, checking aspect ratios/skewness, estimating mesh quality constraints, or planning adaptive mesh refinement for PDE discretization.

Stars 2,009
Forks 275

Install this agent skill to your Project

npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/mesh-generation

SKILL.md

Mesh Generation

Goal

Provide a consistent workflow for selecting mesh resolution and checking mesh quality for PDE simulations.

Requirements

  • Python 3.8+
  • No external dependencies (uses stdlib)

Inputs to Gather

Input Description Example
Domain size Physical dimensions 1.0 × 1.0 m
Feature size Smallest feature to resolve 0.01 m
Points per feature Resolution requirement 10 points
Aspect ratio limit Maximum dx/dy ratio 5:1
Quality threshold Skewness limit < 0.8

Decision Guidance

Resolution Selection

What is the smallest feature size?
├── Interface width → dx ≤ width / 5
├── Boundary layer → dx ≤ layer_thickness / 10
├── Wave length → dx ≤ lambda / 20
└── Diffusion length → dx ≤ sqrt(D × dt) / 2

Mesh Type Selection

Problem Recommended Mesh
Simple geometry, uniform Structured Cartesian
Complex geometry Unstructured triangular/tetrahedral
Boundary layers Hybrid (structured near walls)
Adaptive refinement Quadtree/Octree or AMR

Script Outputs (JSON Fields)

Script Key Outputs
scripts/grid_sizing.py dx, nx, ny, nz, notes
scripts/mesh_quality.py aspect_ratio, skewness, quality_flags

Workflow

  1. Estimate resolution - From physics scales
  2. Compute grid sizing - Run scripts/grid_sizing.py
  3. Check quality metrics - Run scripts/mesh_quality.py
  4. Adjust if needed - Fix aspect ratios, reduce skewness
  5. Validate - Mesh convergence study

Conversational Workflow Example

User: I need to mesh a 1mm × 1mm domain for a phase-field simulation with interface width of 10 μm.

Agent workflow:

  1. Compute grid sizing:
    bash
    python3 scripts/grid_sizing.py --length 0.001 --resolution 200 --json
    
  2. Verify interface is resolved: dx = 5 μm, interface width = 10 μm → 2 points per interface width.
  3. Recommend: Increase to 500 points (dx = 2 μm) for 5 points across interface.

Pre-Mesh Checklist

  • Define target resolution per feature/interface
  • Ensure dx meets stability constraints (see numerical-stability)
  • Check aspect ratio < limit (typically 5:1)
  • Check skewness < threshold (typically 0.8)
  • Validate mesh convergence with refinement study

CLI Examples

bash
# Compute grid sizing for 1D domain
python3 scripts/grid_sizing.py --length 1.0 --resolution 200 --json

# Check mesh quality
python3 scripts/mesh_quality.py --dx 1.0 --dy 0.5 --dz 0.5 --json

# High aspect ratio check
python3 scripts/mesh_quality.py --dx 1.0 --dy 0.1 --json

Error Handling

Error Cause Resolution
length must be positive Invalid domain size Use positive value
resolution must be > 1 Insufficient points Use at least 2
dx, dy must be positive Invalid spacing Use positive values

Interpretation Guidance

Aspect Ratio

Aspect Ratio Quality Impact
1:1 Excellent Optimal accuracy
1:1 - 3:1 Good Acceptable
3:1 - 5:1 Fair May affect accuracy
> 5:1 Poor Solver issues likely

Skewness

Skewness Quality Impact
0 - 0.25 Excellent Optimal
0.25 - 0.50 Good Acceptable
0.50 - 0.80 Fair May affect accuracy
> 0.80 Poor Likely problems

Resolution Guidelines

Application Points per Feature
Phase-field interface 5-10
Boundary layer 10-20
Shock 3-5 (with capturing)
Wave propagation 10-20 per wavelength
Smooth gradients 5-10

Limitations

  • 2D/3D only: No unstructured mesh generation
  • Quality metrics: Basic aspect ratio and skewness only
  • No mesh generation: Sizing recommendations only

References

  • references/mesh_types.md - Structured vs unstructured
  • references/quality_metrics.md - Aspect ratio/skewness thresholds

Version History

  • v1.1.0 (2024-12-24): Enhanced documentation, decision guidance, examples
  • v1.0.0: Initial release with 2 mesh quality scripts

Expand your agent's capabilities with these related and highly-rated skills.

FreedomIntelligence/OpenClaw-Medical-Skills

vcf-annotator

Annotate VCF variants with VEP, ClinVar, gnomAD frequencies, and ancestry-aware context. Generates prioritised variant reports.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

chemist-analyst

Analyzes events through chemistry lens using molecular structure, reaction mechanisms, thermodynamics, kinetics, and analytical techniques (spectroscopy, chromatography, mass spectrometry). Provides insights on chemical processes, material properties, reaction pathways, synthesis, and analytical methods. Use when: Chemical reactions, material analysis, synthesis planning, process optimization, environmental chemistry. Evaluates: Molecular structure, reaction mechanisms, yield, selectivity, safety, environmental impact.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

bio-alignment-io

Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO. Supports Clustal, PHYLIP, Stockholm, FASTA, Nexus, and other alignment formats for phylogenetics and conservation analysis. Use when reading, writing, or converting alignment file formats.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

sleep-analyzer

分析睡眠数据、识别睡眠模式、评估睡眠质量,并提供个性化睡眠改善建议。支持与其他健康数据的关联分析。

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

metabolomics-workbench-database

Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

bio-hi-c-analysis-matrix-operations

Balance, normalize, and transform Hi-C contact matrices using cooler and cooltools. Apply iterative correction (ICE), compute expected values, and generate observed/expected matrices. Use when normalizing or transforming Hi-C matrices.

2,009 275
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results