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.
Best use case
mesh-generation is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using mesh-generation should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/mesh-generation/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How mesh-generation Compares
| Feature / Agent | mesh-generation | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
Related Skills
hypothesis-generation
Generate testable hypotheses. Formulate from observations, design experiments, explore competing explanations, develop predictions, propose mechanisms, for scientific inquiry across domains.
zinc-database
Access ZINC (230M+ purchasable compounds). Search by ZINC ID/SMILES, similarity searches, 3D-ready structures for docking, analog discovery, for virtual screening and drug discovery.
zarr-python
Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.
xlsx
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
writing-skills
Use when creating new skills, editing existing skills, or verifying skills work before deployment
writing-plans
Use when you have a spec or requirements for a multi-step task, before touching code
wikipedia-search
Search and fetch structured content from Wikipedia using the MediaWiki API for reliable, encyclopedic information
wellally-tech
Integrate digital health data sources (Apple Health, Fitbit, Oura Ring) and connect to WellAlly.tech knowledge base. Import external health device data, standardize to local format, and recommend relevant WellAlly.tech knowledge base articles based on health data. Support generic CSV/JSON import, provide intelligent article recommendations, and help users better manage personal health data.
weightloss-analyzer
分析减肥数据、计算代谢率、追踪能量缺口、管理减肥阶段
<!--
# COPYRIGHT NOTICE
verification-before-completion
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
vcf-annotator
Annotate VCF variants with VEP, ClinVar, gnomAD frequencies, and ancestry-aware context. Generates prioritised variant reports.