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 beita6969/ScienceClaw --skill mesh-generationgit clone --depth 1 https://github.com/beita6969/ScienceClawWrote 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/beita6969/scienceclaw/mesh-generation)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/mesh-generation"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/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/beita6969/scienceclaw/mesh-generation"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/mesh-generation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00040 | $0.01202 |
| Opus 5 | $0.00020 | $0.00601 |
| Sonnet 5 | $0.00008 | $0.00240 |
| Haiku 4.5 | $0.00004 | $0.00120 |
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 — 150 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 3.8+
- 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
- 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
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:
- Compute grid sizing:
python3 scripts/grid_sizing.py --length 0.001 --resolution 200 --json - Verify interface is resolved: dx = 5 μm, interface width = 10 μm → 2 points per interface width.
- Recommend: Increase to 500 points (dx = 2 μm) for 5 points across interface.
What ships with it
4 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 · 150 lines · 40 tokens per session scan A b3e7da3717ce
mesh-generation is a skill published in the GitHub repository beita6969/ScienceClaw (898 stars, last pushed 3mo ago), licensed MIT. It adds 40 tokens to every session and 1,202 once invoked, about $0.0002 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 2026-09-03.
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