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 Cai-aa/CAE-Agent-Hub --skill fenics-femgit clone --depth 1 https://github.com/Cai-aa/CAE-Agent-HubWrote 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/cai-aa/cae-agent-hub/fenics-fem)<a href="https://agentmods.dev/skills/cai-aa/cae-agent-hub/fenics-fem"><img src="https://agentmods.dev/badge/skills/cai-aa/cae-agent-hub/fenics-fem/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/cai-aa/cae-agent-hub/fenics-fem"><img src="https://agentmods.dev/badge/skills/cai-aa/cae-agent-hub/fenics-fem.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, 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.
- medium MCP Rug Pull · line 102 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
- medium MCP Rug Pull · line 103 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
- low Tool Misuse · line 103 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
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 | $0.00053 | $0.06002 |
| Opus 5 | $0.00026 | $0.03001 |
| Sonnet 5 | $0.00011 | $0.01200 |
| Haiku 4.5 | $0.00005 | $0.00600 |
Grade A, and why
fenics-fem scanned grade A with 1 finding 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 12d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| Nédélec (edge elements) | `("Nedelec1st", 1)` | Electromagnetics, H(curl) | How it starts
The opening of the file, as written. The whole thing — 643 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FEniCS Finite Element Method for PDEs
TL;DR — Solve partial differential equations with the Finite Element Method (FEM) using FEniCS/dolfinx. Derive the weak form, generate meshes with gmsh, apply Dirichlet/Neumann boundary conditions, solve Poisson or elasticity problems, and export results to XDMF/VTK for ParaView.
When to Use
Use this Skill when you need to:
- Solve elliptic, parabolic, or hyperbolic PDEs on complex geometries
- Implement custom weak forms for multi-physics problems
- Apply mixed Dirichlet/Neumann/Robin boundary conditions
- Perform convergence studies on successively refined meshes
- Export solutions for publication-quality visualization in ParaView
Do not use this Skill when:
- You need a quick 1D finite-difference solution → use SciPy
solve_bvp - You want a spectral method for periodic domains → use pseudo-spectral libraries
- You need GPU-accelerated large-scale CFD → consider OpenFOAM or Fluidity
Background & Key Concepts
Variational (Weak) Form
The FEM converts a strong-form PDE into an integral equation by multiplying by a test function v and integrating by parts. For Poisson's equation:
Strong form: −∇²u = f in Ω, u = uD on ΓD, ∇u·n = g on ΓN
Weak form: Find u ∈ H¹(Ω) such that for all v ∈ H¹₀(Ω): ∫_Ω ∇u·∇v dx = ∫_Ω f v dx + ∫_ΓN g v ds
Function Spaces
| Space | dolfinx name | Use case |
|---|---|---|
| Continuous Galerkin deg 1 | ("Lagrange", 1) |
Scalar fields, temperature |
| Continuous Galerkin deg 2 | ("Lagrange", 2) |
Higher accuracy, elasticity displacement |
| Discontinuous Galerkin | ("DG", 0) |
Cell-wise constants, flux |
| Nédélec (edge elements) | ("Nedelec1st", 1) |
Electromagnetics, H(curl) |
Convergence and Error
For Lagrange P1 elements on a quasi-uniform mesh of size h:
- L² error: O(h²) (one order above approximation degree)
- H¹ error: O(h)
Environment Setup
# Recommended: use conda with conda-forge (dolfinx + gmsh are complex to compile)
conda create -n fenics-env python=3.11 -y
conda activate fenics-env
conda install -c conda-forge fenics-dolfinx mpich petsc4py gmsh pyvista -y
# Verify installation
python -c "import dolfinx; print('dolfinx version:', dolfinx.__version__)"
python -c "import gmsh; print('gmsh version:', gmsh.__version__)"
# For Docker users (simplest approach)
docker pull dolfinx/dolfinx:stable
docker run -it --rm -v $(pwd):/work dolfinx/dolfinx:stable bash
What ships with it
2 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.
- 12d ago First seen · 643 lines · 53 tokens per session scan A 7583f67a2515
fenics-fem is a skill published in the GitHub repository Cai-aa/CAE-Agent-Hub (881 stars, last pushed today), licensed MIT. It adds 53 tokens to every session and 6,002 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
fenics-fem
Use this Skill to solve PDEs with the finite element method using FEniCS/dolfinx: weak form formulation, mesh generation with gmsh, Poisson/elasticity problems, boundary conditions, and paraview export.
finite-element-analysis
Use this Skill for FEA with FEniCSx or scikit-fem: mesh generation, boundary conditions, linear elasticity, heat conduction, and result visualization.
scipy-numerical
SciPy numerical toolkit for physics: ODE/PDE solving, FFT analysis, optimization, numerical integration, and sparse linear algebra with real-world examples.
physicsnemo-discover
Official NVIDIA-authored guidance for navigating PhysicsNeMo — pick the model, datapipe, or example for a SciML/AI4Science task (surrogates, forecasting, downscaling, physics-informed, inverse, generative). Points at existing files via live repo search; never writes code. Do NOT use for installation or environment…
hdf5-pde-data-loading
Patterns for loading PDE simulation datasets (PDEBench, PhiFlow, JAX-CFD) from HDF5 files. Handles layout detection (single tensor vs separate variables), spatial/temporal downsampling, multi-variable systems, HuggingFace and DaRUS data sources, and efficient PyTorch DataLoader creation. Use when preparing PDE data…
physics-units-si
SI units, CODATA constants, dimensional analysis, and rigorous unit conversion. Use when extracting, normalizing, or computing physical quantities (energy, frequency, length, mass, time, temperature). Handles eV/keV/MeV/GeV/TeV, Hz/kHz/MHz/GHz/THz, meter/cm/Å/fm, kg/g/u, second/ms/μs/ns/fs, Kelvin/eV-temperature…