Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add HeshamFS/materials-simulation-skills --skill mesh-generationgit clone --depth 1 https://github.com/HeshamFS/materials-simulation-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/heshamfs/materials-simulation-skills/mesh-generation)<a href="https://agentmods.dev/skills/heshamfs/materials-simulation-skills/mesh-generation"><img src="https://agentmods.dev/badge/skills/heshamfs/materials-simulation-skills/mesh-generation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/heshamfs/materials-simulation-skills/mesh-generation"><img src="https://agentmods.dev/badge/skills/heshamfs/materials-simulation-skills/mesh-generation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00126 | $0.03296 |
| Opus 5 | $0.00063 | $0.01648 |
| Sonnet 5 | $0.00025 | $0.00659 |
| Haiku 4.5 | $0.00013 | $0.00330 |
Grade A, and why
mesh-generation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mesh Generation
Goal
Provide a consistent workflow for selecting mesh resolution and checking mesh quality for PDE simulations.
Requirements
- Python 3.10+
- 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)
All scripts emit a top-level object with inputs (the echoed CLI values) and
results (the computed fields below). Index as result["results"]["..."].
| Script | results Fields |
|---|---|
scripts/grid_sizing.py |
dx, counts (list of per-dimension cell counts, length == dims), notes |
scripts/mesh_quality.py |
aspect_ratio, skewness, size_anisotropy, quality_flags, dims, notes |
mesh_quality.py describes axis-aligned (orthogonal Cartesian) cells defined
purely by edge spacings. For such cells every interior angle is 90°, so the true
angular skewness is always 0.0 and high_skewness is never flagged.
Cell elongation is reported separately via aspect_ratio and the redundant
convenience field size_anisotropy (= 1 - 1/aspect_ratio).
Workflow
- Estimate resolution - From physics scales
- Compute grid sizing - Run
scripts/grid_sizing.py - Check quality metrics - Run
scripts/mesh_quality.py - Adjust if needed - Fix aspect ratios, reduce skewness
- Validate - Mesh convergence study
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 246 lines · 126 tokens per session scan A 8dabf7170b97
mesh-generation is a skill published in the GitHub repository HeshamFS/materials-simulation-skills (66 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 126 tokens to every session and 3,296 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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