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Hpc Fenics

Organization
SciMate-AI
hpc-fenics

Build, review, and debug FEniCS or DOLFINx PDE scripts for finite-element workflows. Use when translating PDEs into UFL, selecting function spaces, applying boundary conditions, choosing between classic FEniCS and DOLFINx, or fixing FEM runtime errors.

Overview

PublisherSciMate-AI
RepositoryHPC-Skills
Skill namehpc-fenics
Stars
97
Forks
10
Bundled files
18
LicenseMIT
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 18 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by SciMate-AI on GitHub. Read the source before you install it.

Installation

Install the Hpc Fenics AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/SciMate-AI/HPC-Skills/tree/main/skills/hpc-fenics
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/SciMate-AI/HPC-Skills.git /tmp/HPC-Skills
mkdir -p .claude/skills
cp -r /tmp/HPC-Skills/skills/hpc-fenics .claude/skills/hpc-fenics
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hpc Fenics in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Hpc Fenics on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Hpc Fenics is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

HPC FEniCS

Treat FEniCS as a family with two main stacks: classic FEniCS and DOLFINx.

Start

  1. Read references/runtime-selection.md first.
  2. Read references/dolfinx-boundary-workflow.md when targeting DOLFINx boundary conditions.
  3. Read references/ufl-and-solver-patterns.md when translating PDEs, choosing spaces, or building forms.
  4. Read references/pde-template-cookbook.md when mapping common PDE classes to known formulation patterns.
  5. Read references/time-dependent-and-nonlinear-patterns.md when building transient or nonlinear solvers.
  6. Read references/implementation-skeletons.md when you need a concrete script shape for classic FEniCS or DOLFINx.
  7. Read references/petsc-solver-playbook.md when choosing linear, nonlinear, or block solver settings.
  8. Read references/mixed-problems-and-output.md when working with mixed spaces, XDMF, or post-processing.
  9. Read references/cluster-execution-playbook.md when staging a FEniCS or DOLFINx script for scheduler-backed cluster execution.
  10. Read references/boundary-io-and-errors.md when repairing runtime failures or resolving IO issues.

Work sequence

  1. Choose one stack and stay consistent:
    • classic FEniCS: fenics or dolfin
    • modern stack: dolfinx
  2. Translate the PDE into the correct weak form before writing code.
  3. Match unknown type and space:
    • scalar field -> scalar space
    • vector field -> vector space
    • mixed formulation -> mixed space with explicit subspace handling
  4. For linear problems, use TrialFunction and TestFunction.
  5. For nonlinear problems, use a Function for the unknown and solve a residual form.
  6. Write outputs in a format the expected post-processor can open, usually XDMF for mesh-coupled fields.

Guardrails

  • Do not mix classic FEniCS imports with DOLFINx APIs in one script.
  • Do not use TrialFunction for a nonlinear unknown.
  • Do not guess a boundary condition if the PDE is under-constrained; say what is missing.
  • Do not ignore shape and rank mismatches in UFL expressions.

Additional References

Load these on demand:

  • references/gmsh-meshtags-and-refinement.md for imported meshes, physical groups, markers, and refinement transfer
  • references/io-visualization-and-writers.md for XDMF, VTK, VTX, and visualization-safe output
  • references/parallel-and-mpi-caveats.md for ownership, boundary marking, and parallel consistency
  • references/space-boundary-output-matrix.md for function-space, boundary-condition, and writer-selection matrices
  • references/cluster-execution-playbook.md for MPI launch, environment pinning, and cluster-side IO planning

Reusable Templates

Use assets/templates/ when a concrete script scaffold is needed, especially:

  • poisson_dolfinx.py
  • transient_diffusion_dolfinx.py
  • fenics-dolfinx-slurm.sh

Outputs

Report:

  • chosen stack and version family if known
  • PDE form and space selection
  • boundary conditions applied
  • expected output files
  • the exact failure class if the script is being repaired

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Hpc Fenics AI skill do?

Build, review, and debug FEniCS or DOLFINx PDE scripts for finite-element workflows. Use when translating PDEs into UFL, selecting function spaces, applying boundary conditions, choosing between classic FEniCS and DOLFINx, or fixing FEM runtime errors.

Why use Hpc Fenics on TypingMind?

Because you install it once and use it with any model. Hpc Fenics is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Hpc Fenics in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/SciMate-AI/HPC-Skills/tree/main/skills/hpc-fenics. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Hpc Fenics?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Hpc Fenics?

As many as you like. As long as a model supports skills, you can use Hpc Fenics with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Hpc Fenics AI skill free?

Yes. It is published on GitHub by SciMate-AI under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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